An Image Reconstruction Algorithm of BF Burden Surface based on Compressive Sensing

Time delay estimation (TDE) is a hot research topic in wireless location technology. Compressed sensing (CS) theory has been widely applied to image reconstruction and direction of arrival estimation since it was proposed in 2004. The sparse model can be constructed in time domain for estimating the time delay by using the CS theory. The measurement matrix plays a crucial role in the processing of signal reconstruction which is the core problem of CS theory. Therefore the research in the measurement matrix has becomes a hotspot in recent years. The existing measurement matrix is mainly divided into two categories, i.e., random measurement matrix and deterministic measurement matrix. The performance of random measurement matrix has bottlenecks. Firstly, because of the redundant measurement matrix data, the generation and storage of the random number put forward a high requirement for hardware. Secondly the random matrix can only satisfy the restricted isometry property in a statistical sense. The research of the deterministic measurement matrix is of great value under this background. The parity check matrix of low density parity check (LDPC) code has good performance in CS theory. However, the method of randomly selecting non-zero element position has a certain probability to generate a measurement matrix with a short loop structure during generating LDPC code measurement matrix. The robustness of the reconstruction performance decreases with the increase of iteration times. A novel quasi-cyclic CS algorithm based on progressive edge-growth is constructed to estimate the time delay. The purpose of this article is to deal with the need to store a large number of data in existing measurement matrix during time delay, by using the CS theory. The algorithm presented here can achieve TDE in a high precision. First, the theoretical bridge between CS and the maximum likelihood decoding is established. And the design criterion of measurement matrix based on the LDPC code is derived. The sparse measurement matrix with quasi-cyclic structure is constructed by introducing the idea of progressive edge-growth. Finally, the orthogonal matching pursuit algorithm is used to estimate the time delay. Furthermore, the computational complexity of the algorithm and the data storage of the measurement matrix are analyzed theoretically. Simulations show that the correct reconstruction probability of the proposed approach is higher than those of the Gauss random matrix and random LDPC matrix under the same dimension. Compared with the random LDPC matrix, the proposed method can improve performance at the expense of less complexity under the condition of the same data storage.

Compressive sensing (CS) is a new technique that give an approach to reconstruct the signal with few numbers of observations or measurements. The CS is based on L1 -norm minimizations to find the sparse solutions and it is known as basis pursuit. In this investigation, CS scheme with different transforms is proposed. The three utilized transform techniques are: Discrete Fourier Transform(DFT), Discrete Cosine Transform(DCT) and Discrete Wavelet Transform(DWT) with daubechies1(DB1) and coiflets1(coif1) basis. The proposed system is tested by employing the following signals: Blocks, Heavy Sine, ‘Bumps’ and ‘Doppler’ which cover wide range of applications. The four testing signals are represented in sparse domain using different transforms. In order to threshold the coefficients of the signals in sparse domain, the universal threshold is utilized in the case of CS with FFT and DWT whereas, the universal threshold is modified to prune the DCT coefficients. The main aim of this study is to investigate the differences among CS with DFT, CS with DCT and CS with DWT, and consequently a suitable transform domain used with CS to be selected. The comparative study is established by assessing the performance of the proposed system using Root Mean Square Error (RMSE), output SNR, and the time required to reconstruct approximated signals. Simulation results have shown that the CS with DWT outperforms the CS with FFT and DCT. CS with DWT has achieved good RMSE values about (0.0014 to 3.359e-8) even when half of the signal elements are removed. CS with FFT and DCT enhanced the noisy Blocks and Bumps signals by 3dB and 1dB respectively, while it is failed to enhance noisy Heavy Sine and Doppler signals. CS with DWT of two basis and for single decomposition level have improved the noisy Blocks, Bumps, Heavy Sine and Doppler signals by 5dB, 4dB, 3dB, and 3dB respectively.

A promising pathway to achieving carbon neutrality while preserving the traditional blast furnace technology for pig iron production lies in the development and implementation of breakthrough innovations in technology, particularly involving the use of hydrogen as a reducing agent and heat source. The use of hydrogen-containing fuel in pig iron production results in significant changes in blast furnace smelting technology, especially in the thermal and reduction processes occurring in the furnace. Enhancing the understanding of these processes when using hydrogen-enriched blast additives and scientifically justifying their optimal quantities to ensure the maximum utilization of hydrogen are critical tasks in developing scientifically substantiated technological principles for using hydrogen in blast furnaces. The development of these principles must be based on research results that examine the effect of increasing hydrogen content in the blast and its distribution across the blast furnace radius on the technological mode of blast furnace smelting. These studies have been conducted by researchers from various countries. The purpose of this research is to summarize and systematize the results of studies on the effect of increasing hydrogen content in the blast and its distribution across the blast furnace radius on the technological mode of blast furnace smelting. The experience of using hydrogen-containing gases in the blast furnace process is reviewed. An analysis of research results regarding the distribution of hydrogen or hydrogen-containing gases across the furnace cross-section and their impact on blast furnace smelting technology is conducted. These studies are categorized as follows: experimental studies on industrial facilities, studies on experimental blast furnaces, numerical studies using mathematical models. The results of experimental studies conducted on industrial blast furnaces and in laboratory conditions, as well as through mathematical modeling, have demonstrated that injecting hydrogen-containing gases increases blast furnace productivity and reduces coke consumption. However, the understanding of hydrogen distribution is ambiguous and sometimes contradictory. The results of some studies indicate that the penetration depth of H2 is limited, with hydrogen primarily concentrating in the furnace wall region, rising with the gas flow, and the injected gas may not reach the furnace center. To enhance the efficiency of hydrogen gas utilization in the blast furnace, it is necessary to increase the penetration depth of hydrogen-containing gas and control the distribution of the gas flow within the furnace. According to alternative viewpoints, the amount of hydrogen decreases from the furnace axis (where H2 levels are highest) toward the walls. The blast furnace smelting process undergoes significant changes compared to the traditional pig iron production process when hydrogen or hydrogen-containing gases are injected. Therefore, operating a blast furnace with hydrogen requires optimization, with one of the key focus areas being the burden charging program tailored to these conditions.

Wearable ECG monitoring devices, often limited by power constraints, require efficient methods to reduce energy consumption and extend battery life for continuous health tracking. This work proposes a digital compressed sensing approach for ECG (DCS-ECG), featuring a suitable sensing matrix, effective sparse signal recovery algorithms, and an optimized number of bits for quantization (BM). The framework is designed to achieve high compression ratios (CR) with minimal signal distortion and parameter extraction without the reconstruction of the original signal. The following five sensing matrices are examined: deterministic block binary diagonal (DBBD) matrix, random binary (RBin) matrix, random Bernoulli (RBen) matrix, random Gaussian (RG) matrix, and random sparse fixed binary (RSB) matrix. These are paired with a sparsifying matrix (ψ), a hybrid of identity matrix (I), discrete sine transform matrix (S), and discrete cosine transform matrix (C) (i.e. ψ = [I S C]). The following five sparse reconstruction algorithms are evaluated: iterative hard thresholding (IHT) algorithm, orthogonal matching pursuit (OMP) algorithm, smoothed ℓ0 (SL0) algorithm, ℓ1 least square (L1LS) algorithm, and subspace matching pursuit (SMP) algorithm. The proposed DCS-ECG system was tested on ECG signals from the MIT-BIH arrhythmia database. The configuration using the DBBD sensing matrix and OMP recovery algorithm with the sparsifying matrix achieved a low average percentage of root mean square difference (PRD) of 3.75 ± 1.43% at CR = 12.5%, which also offers fast reconstruction. For CR = 12.5% and BM = 8, the CS measurement generation on the Arduino Due consumes 2.02 mJ, while reconstruction on the Raspberry Pi requires 1.13 J. The wireless transmission energy is 1.2973 J. Overall, the proposed CS-based system achieves an 86.24% total energy saving compared to the without CS approach, demonstrating its suitability for low-power IoT-enabled wearable edge ECG health monitoring applications.

The ironmaking blast furnace (BF) is an energy-intensive process, requires a considerable amount of carbon-related materials such as coke, coal, or natural gas, and releases enormous amounts of greenhouse gas (GHG). The oxygen enrichment operation is one promising technology to reduce the carbon footprint in the ironmaking process. However, the oxygen enrichment ratio (OER) varies significantly in practices, and the proper one is still unclear, especially in terms of fuel rate saving and in-furnace phenomena. In this study, a multi-fluid BF model is used to quantitatively study the influence of oxygen enrichment on the BF process in terms of bosh gas volume, top gas composition, and inner thermochemical behaviors of solid–gas–liquid phases. Under the simulation conditions, the results show that for every increase of OER, the blast rate is decreased by ~ 85 m3/min and nitrogen content in reducing gas is decreased by ~ 1.13 pct; also, the top gas temperature is lowered by ~ 17 K, and the flame temperature is increased by 58.4 K. Descending of cohesive zone (CZ) inside the BF is observed with the region volume ratio decreased by ~ 0.102; the chemical reserve zone of wustite becomes much narrower and lower inside the BF, but does not disappear. The potential of carbon footprint mitigation in the BF process is discussed with the optimal OER recommended, 7.5 pct under the present simulation conditions. This model can help to build a comprehensive understanding of the fuel rate saving and CO2 emission reduction of a BF adopting an oxygen enrichment operation.

Accurate measurement of the position and shape of the blast furnace (BF) burden surface (BS) is crucial for automated and intelligent BF control. The detection method based on key points of BS can improve the accuracy of radar BS detection in challenging environments with strong interference, ensuring long-term detection accuracy stability across long maintenance intervals. However, transferring a successfully applied detection model from the original BF to a new target BF requires sufficient sample collection in the target BF and model retraining. In practical scenario, data collection is often constrained by plenty of factors like time, space, and personnel. Thus, reducing the number of samples needed for retraining, or implementing model transfer based on small samples, can significantly boost BS detection efficiency. This paper proposes a Conv-Adapter Transfer Net (CATNet) designed for the transfer problem of key point detection models in BFBS and small sample scenarios. The CATNet adopts a newly designed parallel Conv-Adapter transfer module, a small-scale deep learning network with a minimal parameter quantity. Compared to fine-tuning the entire backbone model with a large number of parameters, retraining this transfer module requires far less training data. Therefore, CATNet is more suitable for model transfer in small sample scenarios. The experimental results demonstrate that the CATNet can exhibit strong transfer capability and higher prediction accuracy than the commonly used pretrain-finetune method in small sample scenarios, thereby enhancing the efficiency of model transfer across different BFs.

Real-time and accurate measurement of blast furnace (BF) stockline is important for charging control and BF safety. However, traditional non-contact methods for stockline detection face many challenges such as low accuracy, poor anti-interference ability, and stability in harsh BF environments. The high-temperature novel industrial endoscope (NIE), a visible light detection equipment for BF burden surface (BBS), has captured real-time and continuous video streams of BBS, which provides a brand-new means and ideas for non-contact stockline detection. Based on this, a novel non-contact stockline detection method based on real-time burden surface video streams is proposed. First, an edge detection network based on bootstrap sampling and eigenvector coding clustering (ECC) is established to extract accurate edge distribution of burden surface video streams. Then, an actual imaging region model of the NIE is developed by the latitude circle method to quantify the relationship between the actual imaging region and the BF stockline. Finally, the edge of burden surface video streams is correlated with the corresponding actual imaging region through the NIE imaging model, and the non-contact stockline detection model is derived to realize the BF stockline detection based on video streams. Experiments and industrial applications show that the proposed method is superior to the traditional non-contact stockline detection methods in terms of detection accuracy, stability, and real-time performance.

Due to the application of power electronics and wind power generation equipment in power systems, broadband oscillation events constantly appear, which makes broadband oscillation difficult to detect due to the limitations of communication bandwidth and the sampling theorem. To ensure the safety and stability of the system, and to detect and recognize the broadband oscillation information timely and accurately, this paper presents a multi-mode recognition method of broadband oscillation based on compressed sensing (CS) and the adaptive Variational Mode Decomposition (VMD) algorithm. Firstly, the high-dimensional oscillation signal data collected by the Phasor Measurement Unit (PMU) is compressed and sampled by a Gaussian random matrix, and the obtained low-dimensional data are uploaded to the main station. Secondly, the orthogonal matching pursuit (OMP) algorithm of the master station is used to reconstruct the low-dimension signal, and the original high-dimension signal data are recovered without losing the main features of the signal. Finally, an adaptive VMD algorithm with energy loss minimization as a threshold is used to decompose the reconstructed signal, and the Intrinsic Mode Function (IMF) components with broadband oscillation information are obtained. By constructing oscillating signals with different frequencies, Gaussian white noise with a signal-to-noise ratio of 10 dB to 30 dB is added successively. After the signal is compressed and reconstructed by the proposed method, the signal-to-noise ratio can reach 18.8221 dB to 40.0794 dB, etc., and the oscillation frequency and amplitude under each signal-to-noise ratio can be accurately identified. The results show that the proposed method not only has good robustness to noise, but also has good denoising effect to noise. By using the simulation measurement model, the original oscillation signal is compressed and reconstructed, and the reconstruction error is 0.1263. The basic characteristics of the signal are restored, and the frequency and amplitude of the oscillation mode are accurately identified, which proves that the method is feasible and accurate.

Multiphase flow measurements, based on tomographic imaging techniques, have made substantial progress in the past several years. In particular, ultrasonic tomographic reconstruction has shown its high potential in accurately measuring fluid fractions in a flow cross-section based on direct flow measurements. These types of measurements are very attractive as the system is nonradioactive, has direct contact to fluid, yet without flow obstruction. An ultrasonic tomographic multiphase flow meter (UTM) is being developed to capture the fluid flow rates and phase compositions in producing oil wells. The UTM aims to generate cross-sectional images of the flow inside the pipe. To recover these images, complex computations and high processing time are required, based on a limited amount of available data. For this purpose, compressive sensing (CS) reconstruction methods have shown great potential in overcoming the mentioned data quality and quantity issues. CS builds upon the fact that one can recover certain data of interest from potentially very few measurements. Many CS algorithms have been proposed in the literature. In this paper, we seek to find a suitable algorithm by comparing different CS algorithms in terms of the resulting recovered image quality. Specifically, we survey three algorithms in CS, namely basis pursuit, iterative soft thresholding (IST), and orthogonal matching pursuit (OMP). We also compare the results of the CS algorithms to that of a least-square based conventional algorithm. The numerical results show the superiority of CS recovery algorithms over the conventional approach, both qualitatively and quantitatively. The results also suggest that high image quality could potentially be achieved with less complex CS algorithms, i.e., the IST and OMP algorithms. As presented in the paper, our goal is to adapt CS reconstruction algorithms for images generated by the UTM, for the purpose of accurate quantification of oil, gas, and water phase fractions produced from different wells.

The recovery algorithm is a crucial issue of the compressed sensing (CS). This paper presents a greedy algorithm called optimized orthogonal matching pursuit (OOMP) for sparse signal recovery. The OOMP algorithm improves the orthogonal matching pursuit (OMP) algorithm via providing the projection onto the subspace generated by the selected measurements and minimizing the corresponding residual error at each iteration. Compared with the OMP algorithm, the simulation results show that the proposed algorithm provides a better approximation of a given signal and reduces measurements needed to recover the signal accurately.

To achieve high capacity, high speed, and secure image transmission, we propose a multi-image computational ghost imaging (CGI)-based encryption scheme that integrates compressed sensing (CS), block scrambling, and dynamic-salt-driven bidirectional XOR diffusion. First, multiple images are partitioned into 8 × 8 pixel blocks, and their spatial structure is disrupted through random scrambling. The scrambled composite image then undergoes pixel-level encryption via two-round bidirectional XOR diffusion, using session-unique keys derived from SHA-256-based dynamic salt, eliminating the statistical characteristics of the original images. Subsequently, each pixel block is subjected to both Gaussian CS and Hadamard-based CGI measurements in parallel, achieving dual-mode compressive encryption and enhancing robustness through measurement redundancy. Finally, only the scrambling key, the XOR-diffusion key, and the compressed measurements are stored; the original image information is thus transformed into unrecognizable measurement data. During the decryption process, the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) with a Discrete Cosine Transform (DCT) sparse basis is employed for dual-sparse reconstruction from the compressed measurements, recovering the encrypted composite image. An inverse XOR operation is then applied to remove the pixel-level diffusion, followed by block reordering using the scrambling key to restore the original images. Experimental results demonstrate that the proposed scheme enables efficient and secure multi-image transmission while maintaining high decrypted image quality. Security analysis indicates that the scheme possesses high key sensitivity, effectively resisting chosen-plaintext attacks. Histogram uniformity analysis and cropping attack resistance experiments further confirm its excellent statistical security and robustness.

5There is great concern currently about the presence of cyanides (salts of hydrocyanic acid) and other compounds containing the CN radical in metallurgical emissions. Cyanides are highly toxic and have a very deleterious impact on human health and on the environment. Cyanide formation is highly likely in ferrous metallurgy. In particular, cyanides may be formed from the components involved in blast-furnace processes: carbon, nitrogen, hydrogen, and alkali metals. The temperature in the working space of the blast furnace varies widely: from 100‐200 ° C (at the charge hole) to 2000‐ 2300 ° C (in the tuyeres). Various compounds containing the CN group are stable within this region. Analysis of the chemical composition of metallurgical emissions confirms the presence of cyanides. Thus, in the water of the slurry tank at Kosogorsk metallurgical plant, an elevated concentration of CN radicals is observed. This tank contains the byproducts obtained in purifying blast-furnace gas: charge-hole dust and slurry. In the present work, we analyze the thermodynamic conditions of cyanide stability in various temperature zones of the blast furnace, for different compositions of the batch and atmosphere, using IVTANTERMO thermodynamic-simulation software. This software permits the calculation of complex chemical equilibria in multicomponent, multiphase systems. The equilibrium composition of the reagents in the system may be calculated for specified temperature, pressure, and concentrations of the elements. The computational algorithm is based on minimizing the Gibbs energy of the system. The distinguishing feature of the IVTANTERMO software used in the present work is that the thermodynamic parameters employed are obtained by the analysis of primary data in the literature [1, 2]. A special calculation procedure has been developed for thermodynamic simulation of the blast-furnace process. The chemical elements in the system are selected on the basis of information regarding their presence in the blast furnace for specified conditions. Thus, the initial system includes nine elements: C, O 2 , H 2 , N 2 , Fe, K, Na, Cl, and F. Preliminary calculations show that the other elements in the blast furnace, such as Si, Ca, Al, Mg, Mn, etc., have little influence on the formation and decomposition of materials containing cyanides. Research shows that most of the cyanides formed in the blast furnace are the compounds CN, CN 2 , HCN, KCN, and NaCN. The salts NaCN and KCN and hydrocyanic acid HCN are most stable, i.e., correspond to the broadest interval of existence with variation in the external conditions, such as temperature, pressure, chemical composition, and redox potential. Thermodynamic analysis indicates that the quantity of each material formed in the blast furnace is determined by the following parameters:

There is a huge increase in data in terms of data conversion that effects the performance and complexity of the devices like analog-to-digital converters (ADC).The standard ADC uses the conventional Shannon-Nyquist Theorem which says that sampling frequency should be twice of the maximum frequency. These samples require very huge storage.Compressive sensing(CS) which gives solution to this problem. In CS we decrease the sampling rate much less than the Nyquist rate and reconstruct the original signal.There are different reconstruction algorithms evolved since from its origin each. In this paper, we are making a comparison between the performances of Orthogonal Matching Pursuit(OMP) and Regularised Orthogonal Matching Pursuit(ROMP) algorithms for image reconstruction.

Due to the broad bandwidth nature of the direct sequence spread spectrum (DSSS) signal, the application of narrowband interference (NBI) cancelation algorithms based on the Nyquist‐Shannon sampling theory are limited by the high sampling rate. To solve the sampling and processing difficulty in the traditional DSSS communication interference cancelation method, we used the compressive sensing (CS) to reduce the sampling rate of the DSSS communication system, and further proposed a compressed domain NBI elimination method in this study. We proved that NBI with a certain bandwidth and DSSS signal are both sparse in specific dictionaries and proposed the corresponding sparse dictionary construction method. Detailed analysis of the compressed domain features of NBI and the DSSS signal demonstrated that it is possible to extract the strong NBI components from the compressed measurements. Two compressed domain NBI cancelation algorithms were proposed based on this idea. The extracted NBI components were filtered out from the compressed measurements directly in the first algorithm, and then we achieved the DSSS signal demodulation in compressed domain using the separated DSSS signal components with the orthogonal matching pursuit (OMP) algorithm. A proposed extraction filter block sparse Bayesian learning (EFBSBL) framework algorithm was utilized to estimate the NBI from the compressed measurements in the second algorithm. The estimated NBI was eliminated from the received signal, and then we recompressed the DSSS signal after NBI cancelation and realized the DSSS signal demodulation in compressed domain with the OMP algorithm. Simulation results support our theoretical analysis results and reveal that the two proposed algorithms are both effective for NBI cancelation and significant interference cancelation performance are achieved.

In this paper, we present a novel architecture based on field-programmable gate arrays (FPGAs) for the reconstruction of compressively sensed signal using the orthogonal matching pursuit (OMP) algorithm. We have analyzed the computational complexities and data dependence between different stages of OMP algorithm to design its architecture that provides higher throughput with less area consumption. Since the solution of least square problem involves a large part of the overall computation time, we have suggested a parallel low-complexity architecture for the solution of the linear system. We have further modeled the proposed design using Simulink and carried out the implementation on FPGA using Xilinx system generator tool. We have presented here a methodology to optimize both area and execution time in Simulink environment. The execution time of the proposed design is reduced by maximizing parallelism by appropriate level of unfolding, while the FPGA resources are reduced by sharing the hardware for matrix-vector multiplication across the data-dependent sections of the algorithm. The hardware implementation on the Virtex6 FPGA provides significantly superior performance in terms of resource utilization measured in the number of occupied slices, and maximum usable frequency compared with the existing implementations. Compared with the existing similar design, the proposed structure involves 328 more DSP48s, but it involves 25,802 less slices and 1.85 times less computation time for signal reconstruction with N = 1024, K = 256, and m = 36, where N is the number of samples, K is the size of the measurement vector, and m is the sparsity. It also provides a higher peak signal-to-noise ratio value of 38.9 dB with a reconstruction time of 0.34 s, which is twice faster than the existing design. In addition, we have presented a performance metric to implement the OMP algorithm in resource constrained FPGA for the better quality of signal reconstruction.