Error Ratio Research Articles

A novel lidar signal denoising method based on variational mode decomposition optimized using whale algorithm

Original lidar return signals are covered by high levels of noise that seriously affect the accuracy of subsequent data processing and inversion. Therefore, it is important to separate the effective signal from the returned signal with noise interference. In this paper, an efficient denoising method based on the variational mode decomposition (VMD) algorithm optimized using the global search strategy-based whale algorithm and the total variational stationary wavelet transform (GSWOA-VMD-SWTTV) is proposed, and this method is applied to denoising of lidar return signals. First, the global search strategy-based whale optimization algorithm (GSWOA) is used to acquire the optimal parameters of the VMD algorithm adaptively, and the lidar return signal is then decomposed by global search strategy-based whale optimization algorithm (GSWOA)-VMD. The effective modal components are then determined using the cross-correlation coefficient method from the decomposed modal components, and total variation stationary wavelet denoising is performed on each effective mode. Finally, the effective modes are reconstructed to obtain a clean lidar return signal. Moreover, to provide further verification of the effectiveness of the proposed method, it is compared with the ensemble empirical mode decomposition (EEMD) method, the complete EEMD with adaptive noise (CEEMDAN) method, the singular value decomposition (SVD) method, and the wavelet threshold method under sunny, cloudy, and dusty weather conditions. The experimental results demonstrate the superior noise reduction performance of the proposed algorithm, which can filter out strong noise from the signal while retaining the complete signal details without distortion; additionally, the proposed method has the highest signal-to-noise ratio and lowest mean square error.

Study on the error characteristics of transformer for primary and secondary fusion used in pole-mounted switchgear

The fusion of primary and secondary functions refers to integrating the function of primary equipment and secondary equipment together. If doing so, the working condition of the equipment for primary and secondary fusion becomes more and more complex and bad, especially the transformer working in the smaller workspace after fusing. Therefore, this paper focuses on the transformer used in primary and secondary fusion pole-mounted Switchgear and establishes the temperature field and electric field simulation model of the primary and secondary fusion pole-mounted switchgear transformer, respectively, and the temperature rise and the environment electric field interference influencing the error of transformer are analyzed as well. When the working environment temperature ranges from -40 to 70°C, the maximum error of low-power current transformers and resistive voltage transformers is 0.1246% and 0.0371%, respectively. The electric field is one of the main factors influencing resistive voltage transformers, which makes ratio errors and angle errors -0.256% and -22.5392’, respectively. The research reveals that under conditions, the error is mainly dependent on temperature rise and electric field. These two factors have a great influence on the error, while the transformers still have enough reliability.

Size specific dose estimation in pediatric CT: preliminary study and conversion factors.

The objective of this paper is to compare the differences between volumetric CT dose index (CTDIVOL) and size-specific dose estimate (SSDEWED) based on water equivalent diameter (WED) in radiation dose measurement, and explore a new method for fast calculation of SSDEWED. The imaging data of 1238 cases of head, 1152 cases of chest and 976 cases of abdominopelvic were analyzed retrospectively, and they were divided into five age groups: ≤ 0.5, 0.5~≤ 1, 1~≤ 5, 5~≤ 10 and 10~≤ 15years according to age. The area of interest (AR), CT value (CTR), lateral diameter (LAT) and anteroposterior diameter (AP) of the median cross-sectional image of the standard scanning range and the SSDEWED were manually calculated, and a t-test was used to compare the differences between CTDIVOL and SSDEWED in different age groups. Pearson analyzed the correlations between DE and age, DE and WED, f and age, and counted the means of conversion factors in each age group, and analyze the error ratios between SSDE calculated based on the mean age group conversion factors and actual measured SSDE. The CTDIVOL in head was (9.41±1.42) mGy and the SSDEWED was (8.25±0.70) mGy: the difference was statistically significant (t=55.04, P<0.001); the CTDIVOL of chest was (2.68±0.91) mGy and the SSDEWED was (5.16±1.16) mGy, with a statistically significant difference (t=-218.78, P<0.001); the CTDIVOL of abdominopelvic was (3.09±1.58) mGy and the SSDEWED was (5.89±2.19) mGy: the difference was also statistically significant (t=-112.28, P<0.001). The CTDIVOL was larger than the SSDEWED in the head except for the ≤ 0.5year subgroup, and CTDIVOL was smaller than SSDEWED within each subgroup in chest and abdominopelvic. There were strong negative correlations between f and age (head: r=-0.81; chest: r=-0.89; abdominopelvic: r=-0.86; P<0.001). The mean values of f at each examination region were 0.81~1.01 for head, 1.65~2.34 for chest and 1.71~2.35 for abdominopelvic region. The SSDEWED could be accurately estimated using the mean f of each age subgroup. SSDEWED can more accurately measure the radiation dose of children. For children of different ages and examination regions, the SSDEWED conversion factors based on age subgroup can be quickly adjusted and improve the accuracy of radiation dose estimation.

Control-oriented thermal model and load identification for circulating cooling water system in precision process applications

With the increasing precision in machining and measurement, there is a growing demand for improved temperature stability in cooling water systems used in industrial processes. Accurate thermal models and load states are essential for developing efficient controllers to enhance temperature performance. This paper focuses on the development of thermal models for various components of circulating water systems in precision water-cooling processes. It presents a control-oriented thermal model framework that segregates the water circulation cycles and defines system inputs and processes. A novel identification algorithm is proposed to address thermal inertia and cyclic nonlinearity by extracting the cyclic period and cycle change rate. The algorithm can simultaneously estimate the state–space model and unmeasurable load disturbances. A validation experimental setup was constructed, and the results demonstrate that, for different flow scenarios, the estimated load and thermal model parameters align well, with correlations exceeding 0.85 and error ratios close to 10%. Additionally, the circulating water output temperature can be accurately predicted. The fitting degrees surpass 84% within five cycles, with correlations exceeding 0.97 and error standard deviations below 0.22 °C.

Photonic-assisted high-order vector millimeter-wave signal generation enabled by DSM.

We propose an optical dual-single-sideband (dual-SSB) modulated 16384-quadrature amplitude modulation (QAM) photonic vector millimeter-wave (mm-wave) signal generation scheme based on delta-sigma modulation (DSM). With the aid of the DSM, the severe nonlinear distortion of envelope detection for high-order QAM modulation signals in wireless communication can be effectively resolved. For the validation of our proposed scheme, we experimentally demonstrate the generation of a 40 GHz 16384-QAM orthogonal frequency division multiplexing (OFDM) photonic vector mm-wave signal and transmission over a 25-km standard single-mode fiber (SSMF), and a 1-m wireless link with the bit error ratio (BER) reaches the hard-decision forward-error-correction (HD-FEC) threshold of 3.8 × 10-3.

Simplified toxicity assessment in pharmaceutical and pesticide mixtures: Leveraging interpretable structural parameters

The potential toxicity arising from antibiotics and pesticides poses a significant risk to the preservation of groundwater. This study investigates the effects of binary mixtures of pharmaceuticals and pesticides by assessing their log EC50, log EC30, and log EC10 values in relation to Vibrio fischeri bacteria. Based on a comprehensive dataset of 459 observations, this work identifies suitable simple descriptors. Rigorous statistical analysis confirms the models’ reliability, accuracy, precision, and favorable goodness-of-fit. Notably, the ratios of coefficient of determination (R2) for the novel models compared to the best comparative models exceed 1.0: 0.8618/0.8085 for log EC50, 0.8856/0.8422 for log EC30, and 0.8973/0.8556 for log EC10. Additionally, the ratios of root mean square error (RMSE) for the new models relative to their counterparts are all below 1.0: 0.159/0.191 for log EC50, 0.131/0.169 for log EC30, and 0.182/0.215 for log EC10.

Empirical curvelet transform based deep DenseNet model to predict NDVI using RGB drone imagery data

Predicting accurately the Normalized Difference Vegetation Index (NDVI) trends from RGB images are essential to monitor crops and identify issues related to plant diseases, and water shortages. The current NDVI prediction models are primarily based on traditional machine learning models which lack reliability due to the problem related to atmospheric conditions. To predict NDVI in Prince Edward Island using RGB drone imagery data, this paper proposed a novel framework integrating empirical curvelet transform and DenseNet models. Each channel of RGB drone imagery data was passed through empirical curvelet transform method where the curvelet coefficients were analysed which result in creating a new formula to design NDVI. The output of the new formula was sent to the deep DenseNet to predict the final NDVI. The proposed model was evaluated using quantitative metrics including, Q-Q plot, regression, correlation coefficients, structural similarity (SSIM), peak signal to noise ratio (PSNR) and mean square error (MSE) as well as accuracy (ACC), sensitivity (SEN), f1-score, specificity. The obtained results showed that the proposed model outperformed the previous models by scoring the highest values of SSIM = 0.98, and lowest MSE = 120. It is believed that the proposed model is helpful to support farmers in monitoring the growth and plant health as well as to identify crops problems.

Broiler sound signal filtering method based on improved wavelet denoising and effective pulse extraction

There is little attention paid to signal filtering in existing broiler health monitoring or broiler sound signal classification research, and the only few studies still have issues such as lack of specific, in-depth, and specialized details. In response to these problems, the authors conducted a depth analysis of the signal characteristics of effective signal components and noises, and proposed a broiler sound signal filtering method based on improved wavelet denoising and effective pulse extraction. This method consists of two parts. The first part is wavelet denoising, which is used to remove the widely distributed noises in broiler sound signals. Specifically, a wavelet denoising algorithm based on continuous differentiable threshold function and scale threshold has been proposed. The disadvantages in existing hard threshold function, soft threshold function, and semi-soft threshold function have been effectively addressed, and the smoothness of broiler sound signals has been ensured. The second part is pulse extraction processing, which is used to remove sudden noises that appears as independent pulses in broiler sound signals. Specifically, a two-stage variable multi-threshold pulse extraction algorithm has been proposed. The main advantages of existing three-threshold pulse extraction algorithm and double-threshold pulse extraction algorithm have been referenced, the threshold setting standard has been clarified, the continuous multi-pulse problem has been given special attention, and the effective signal components in broiler sound signals have been accurately extracted. In this way, the noises in broiler sound signals were removed, and the effective signal components were highlighted. The signal-to-noise ratio (SNR) and root mean square error (RMSE) from signal detection field, as well as the classification accuracy and F1-score from machine learning field, were used to comprehensively evaluate the effect of the signal filtering method proposed in this paper. A large number of tests shown that, compared to existing signal filtering methods, the broiler sound signal processed by the signal filtering method proposed in this paper achieved the maximum SNR and the minimum RMSE. On feature data sets created from broiler sound signals processed by different signal filtering methods, three classifiers all achieved the highest classification accuracy and F1-score on the feature data set corresponding to the signal filtering method proposed in this paper. The feasibility, practicality, and superiority of the signal filtering method proposed in this paper have been widely verified. This study is an important supplement to the broiler health monitoring and broiler sound signal classification research, providing important references for signal filtering research in similar fields.

Application of Fourier-Transform Infrared Spectroscopy for the Assessment of Wine Spoilage Indicators: A Feasibility Study.

Wine aroma is one of the most frequently used and explored quality indicators. Typically, its assessment involves estimating the volatile composition of wine or highly trained assessors conducting sensory analysis. However, current methodologies rely on slow, expensive and complicated analytical procedures. Additionally, sensory evaluation is inherently subjective in nature. Therefore, the aim of this work is to verify the feasibility of using FTIR spectroscopy as a fast and easy methodology for the early detection of some of the most common off-odors in wines. FTIR spectroscopy was combined with partial least squares (PLS) regression for the simultaneous measurement of isoamyl alcohol, isobutanol, 1-hexanol, butyric acid, isobutyric acid, decanoic acid, ethyl acetate, furfural and acetoin. The precision and accuracy of developed calibration models (R2P > 0.90, range error ratio > 12.1 and RPD > 3.1) proved the ability of the proposed methodology to quantify the aforementioned compounds.

Experimental reservoir computing with diffractively coupled VCSELs.

We present experiments on reservoir computing (RC) using a network of vertical-cavity surface-emitting lasers (VCSELs) that we diffractively couple via an external cavity. Our optical reservoir computer consists of 24 physical VCSEL nodes. We evaluate the system's memory and solve the 2-bit XOR task and the 3-bit header recognition (HR) task with bit error ratios (BERs) below 1% and the 2-bit digital-to-analog conversion (DAC) task with a root mean square error (RMSE) of 0.067.

Big data-assisted student’s English learning ability appraisal model using fuzzy logic system

The ability, interest, and prior accomplishments of students with varying proficiency levels all impact how they learn English. Exact validation is essential for facilitating efficient evaluation and training models. The research’s innovative significance resides in incorporating personal attributes, progressive appraisal, and Fuzzy Logic-based appraisal in English language learning. The PA2M model, which addresses the shortcomings of existing models, offers a thorough and accurate assessment, enabling personalized recommendations and enhanced teaching tactics for students with varied skill levels. This research proposes the Fuzzy Logic System (FLS)-based Persistent Appraisal Assessment Model (PA2M). Based on the students’ evolving performance and accumulated data, this model evaluates the students’ English learning capabilities. The model assesses the student’s ability using fuzzification approaches to reduce variations in appraisal verification by linking personal attributes with performance. Mamdani FIS offers a clear and thorough evaluation of student’s English learning capacity within the framework of the appraisal methodology. The inputs are updated utilizing performance and accumulated ability data to improve validation consistently and reduce converge errors. During the fuzzification process, pre-convergence from unavailable appraisal sequences is eliminated. The PA2M approach determines precise improvements and evaluations depending on student ability by merging prior and current data. Several appraisal validations and verifications result in clear fresh suggestions. According to experimental data, the suggested model enhances 9.79% of recommendation rates, 8.79% of appraisal verification, 8.25% of convergence factor, 12.56% error ratio, and verification time with 8.77% over a range of inputs. The PA2M model provides a fresh and useful way to evaluate English learning potential, filling in some gaps in the body of knowledge and practice.

Lidar signal processing method for atmospheric coherence length measurement based on the WD-ADMF

A denoising method applied to atmospheric coherent length lidar is proposed. Wavelet decomposition (WD) and the adaptive median filter (ADMF) are combined in this method. In this research, the effectiveness of the WD-ADMF has been verified through simulation and measurement. The results show that this filter algorithm, when applied to lidar data, improves the average peak signal-to-noise ratio (PSNR) and centroid error while maintaining data integrity such that the measurement of coherence length or the inference of C n 2 from coherence length more closely matches simulated truth and measured data.

The method for precise seismic detection of geological structures in underground coal mines and application

The occurrence of major accidents such as water inrush in coal mines and coal-gas outbursts is closely correlated with the unclear exploration of underground geological structures. Seismic exploration in underground coal mines is not limited by ground conditions and close to the detected target, so it has advantages with respect to the detection of underground geological structures. However, the geological structures leading to accidents are usually characterized by small size and diverse combinations, making it difficult to detect. In coal mine working faces, the use of transmission observation systems, combined with full waveform inversion methods, represents a new attempt at finely detecting geological structures. Therefore, a three-dimensional numerical model of an abnormal combination of faults and collapse columns was established, a physical simulation model of a multi-collapse column combination was built, and transmission and detection experiments were carried out. The results show that the morphology and locations of 1 fault and 4 collapse columns obtained by the FWI algorithm are consistent with the data from the actual numerical model. According to physical simulation results, the background velocity of the model after inversions and the velocity of 5 anomalies are consistent with the actual values. Field experiments were conducted in the actual underground coal mines. After verification, the average error ratios of the long axis, short axis and intersection angle of the 4 collapse columns were 0.19, 0.15 and 0.02. The detection findings and comparison results confirm the feasibility of precise detection of geological structures by transmissive seismic waves based on the FWI method.

Probing dark matter with adaptive-optics based flux ratio anomalies: photometric and astrometric precision

ABSTRACT Strong gravitational lensing is a powerful probe of the distribution of matter on sub-kpc scales. It can be used to test the existence of completely dark sub-haloes surrounding galaxies, as predicted by the standard cold dark matter model, or to test alternative dark matter models. The constraining power of the method depends strongly on photometric and astrometric precision and accuracy. We simulate and quantify the capabilities of upcoming adaptive optics systems and advanced instruments on ground-based telescopes, focusing as an illustration on the Keck Telescope (OSIRIS + KAPA, LIGER + KAPA) and the Thirty Meter Telescope (TMT; IRIS + NFIRAOS). We show that these new systems will achieve dramatic improvements over current ones in both photometric and astrometric precision. Narrow line flux ratio errors below 2 per cent, and submilliarcsecond astrometric precision will be attainable for typical quadruply imaged quasars. With TMT, the exposure times required will be of order a few minutes per system, enabling the follow-up of 100–1000 systems expected to be discovered by the Rubin, Euclid, and Roman Telescopes.

Chaotic Spread-Spectrum Communication: A Comparative Study between Chaotic Synchronization and Matched Filtering

This publication investigates the performance of demodulation methods utilized in spread spectrum chaotic communication systems in order to understand conditions at which advanced demodulation methods, such as chaotic synchronization, provide tangible benefits over classical, matched filtering-based approaches. We conduct simulations and comparisons of three different communication systems: classic direct sequence spread spectrum (DSSS), chaotic signal fragment-based pseudo-chaotic spread spectrum (PCSS), and chaotic synchronization-based antipodal chaos shift keying (ACSK). These systems possess similar spectral and time domain characteristics, allowing us to shed light on their fundamental differences and limitations in chaos-based communication. Additionally, we assess the impact of frequency modulation (FM) on these modulation methods, as FM allows the creation of simplified non-coherent modulation schemes. Our findings, based on the analysis of bit error ratio (BER) curves, demonstrate that in the case of a non-dispersive communication channel, the utilization of chaotic synchronization does not allow to achieve performance of correlation-based receivers. Additionally, the utilization of chaotic synchronization for multiple access poses certain challenges due to malicious synchronization between users. As a supplementary finding, we show that in systems with matched filter-based demodulation, discrete-time quantized spreading sequences confer an advantage over analogous, continuous-time spreading waveforms.

Evaluations of Crypto-System AES Using Multiple Bloc Ciphering Mode

Cryptography, a scientific field that has existed even before the beginning of computer science. This article looks at the development symmetric crypto-system, which falls within the framework of image security, by Advanced Encryption Standard (AES- Advanced Encryption Standard) algorithms. AES algorithm is not integrity protected. Cryptanalysis could use modified encrypted image of each ciphered bloc for generating oracle and getting the key. A new crypto-system uses hash function named AES-GCM (Advanced Encryption Standard-) for solving this problem. Using Galois Counter Mode (GCM) combined with Secure Hash Algorithm 256 bits (SHA-256) or BLAKE2s hash function, the old mode of ciphering like: Cipher FeedBack (CFB), Output FeedBack (OFB), Cipher Block Chaining (CBC), Electronic Codebook Block (ECB) and CounTeR (CTR) mode encryption, will increase the security level at confidentiality and integrity. In this article, robustness of the crypto-system will be evaluated by multiple criteria, indeed the statistical analysis, sensitivity measurement and performance measurement. For the statistical analysis, the histogram is flat and the correlation between adjacent pixel is not linear for the encrypted image. The relation between clear image and encrypted image doesn’t exist. For the sensitivity analysis, Number of Pixel Changing Rate (NPCR) and Unified Averaged Changed Intensity (UACI) permit to avoid differential attack of the crypto-system. The two values are respectively near 99% and 30%. For performance measurement, the similarity of the decrypted image and original image will be tested. The Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are equals of infinity and zero. The decrypted image and original image are the same. The integrity of the image on the crypto-system will be evaluated by modifying one bit of the encrypted image. The decryption process doesn’t give decrypted image and show that the tag value is incorrect.

Disturbance Observer-Based Tracking Control of High-Speed Trains Under Adhesion Dynamics

As the anti-skid control and speed tracking control of high-speed trains (HSTs) are interrelated and mutually constrained, it is very challenging to achieve simultaneous tracking of speed and slip ratio. In this paper, it is proposed for the first time that when the traction braking force has been limited to a reasonable range by the adhesion anti-skid condition, the high-precision tracking of the position and velocity curves of HSTs can be achieved, and the tracking error of the slip ratio is acceptable. Firstly, a novel modelling of the adhesion dynamics of HSTs under realistic gusts of wind disturbance is developed, and a model based on strict feedback is obtained by linearising the feedback for high-order non-linear systems. Then, this paper first presents the design scheme of fast terminal sliding mode controller based on disturbance observer for HSTs, which can ensure the anti-disturbance performance and accurate tracking of HSTs. Finally, an example is used to verify the validity of the results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The actual traction/braking force of HSTs is provided by adhesion, and the existing literature on slip ratio tracking essentially sacrifices the tracking performance of the traction/braking system to meet the anti-skid requirements, while considering velocity and position tracking alone may leave the train in an undesired condition such as “idling” or “skidding”. This paper seeks to achieve high accuracy tracking of velocity and position profiles of HSTs under restricting the slip ratio to a reasonable range, which is expected to be an effective method for train tracking controller design. To avoid discomfort to passengers, nonlinearities dynamics are considered and an anti-disturbance controller is designed to compensate the impact of natural wind disturbances. To enhance the practicality, feedback linearization is introduced to reduce the complexity of the controller implementation. The designed fast terminal sliding mode controller also avoids jitter and singularity problems and obtains finite time stability. The stability analysis and numerical simulation have confirmed the effectiveness of the proposed scheme, which still needs to be verified by experiments in the future.

ANALYSIS OF MEASUREMENT ACCURACY DURING VERIFICATION OF MIT TEMPERATURE METERS

The paper presents an algorithm and the results of calculating the confidence limits of the measurement error of resistance (voltage) during verification of multichannel precision temperature meters MIT 2 and MIT 8. It is established that the actual error value of the meter in operation can exceed the permissible value by 4.3 times. In the temperature equivalent, the additional error can reach 130% of the limit of the basic permissible error of the reference platinum resistance thermometer PTS-10M of the first category. The necessity of attributing the measurement results by MIT meters to the most responsible measurements, for which even the minimum excess of the permissible error is not allowed, is justified. The corresponding decision-making rule has been formulated. The minimum ratio of errors of the reference and verifiable measuring instruments has been determined, in which the probability of erroneous recognition of an obviously suitable meter as unsuitable does not exceed 0.3 – the limit set in MI 186-88 "GSI. Measuring instruments. Setting the parameters of the verification methods" values. The required error ratio is no worse than 1/2. For widely used meters MIT 2.05, MIT 8.10, MIT 8.15, methods are proposed to improve the accuracy of measurements and ensure the required reliability of verification using recommended standards. To verify the relatively new and most accurate meters MIT 8.20, MIT 8.30, the need to use a secondary resistance standard is justified. The work can be useful to specialists involved in the development, operation of temperature measuring instruments and their metrological support.

Secured finance handling for supply chain integrated business intelligence using blockchain application scenarios

Business intelligence is becoming more essential for supply chain administrators to make good decisions. The globalization of supply chains makes their management and control more challenging. Blockchain is a distributed digital ledger technology that guarantees traceability, transparency, and security and promises to ease global supply chain management issues. This paper proposes the Blockchain-assisted Secure Data Management Framework (BSDMF) for financial data handling for supply chain integrated business intelligence models. Analyzing, collecting, and demonstrating data could be important to a business, its supply chain performance, and sustainability. The blockchain can interrupt supply chain processes for improved finance handling, distributed management, and process automation. The study’s experimental result will help organizations deploy blockchain applications with intelligent business strategies to support supply chain management effectively. The simulation outcome has been implemented, and the recommended method achieves a computation time of fewer than 2 hours, an efficiency ratio of 97.4%, an error ratio of 94.1%, data authentication of 92.1%, and a data management ratio of 98.7%.

A Quasi-Cyclic LDPC Based Low Complexity and Area-Efficient Communication System for IEEE 802.11n

Low-density parity-check (LDPC) code, which has an excellent error-correcting performance that is close to the Shannon limit, is the most often used error correction code (ECC) for reliable and effective communication. Despite higher performance and lower decoding complexity, the main disadvantage of LDPC codes is their high encoding complexity. A significant problem is the VLSI implementation of the LDPC encoder and decoder. In this paper, structured LDPC codes—also known as quasi-cyclic low-density parity check codes—have been used since it is good bit error ratio (BER) performance and adaptable hardware execution characteristics. The Complete (Encoder-Channel-Decoder) communication system with a 1/2 code rate, 648 bits codeward length, and sub-block size of z = 27 has been constructed for the IEEE 802.11n wireless standard.