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When backfilling narrow spaces, controlled low-strength materials (CLSM) can be used to achieve an effective backfilling effect. The pipeline engineering in Yahnghe Avenue of Suqian, China, provides a favorable on-site condition for the use of CLSM. However, no guidance exists for the determination of the material mixture ratio of CLSM for this geological condition. Laboratory tests were performed to investigate the basic physical parameters of excavated soil and the optimal mixture ratio of CLSM. Results indicate that the sand and silt account for 29.76% and 57.23% of the weight of excavated soil, respectively. As the water content increases (from 40% to 50%), the flowability of the CLSM approximately shows a linear increase (slumps values from 154.3 mm to 269.75 mm for 9% cement content), while its compressive strength shows a linear decreasing trend (from 875.3 KPa to 468.3 KPa after curing for 28 days); as the cement content increases (from 6% to 12%), the flowability approximately shows a linear decreasing trend (from 238.8 mm to 178.5 mm for 45% water content), while the compressive strength shows a linear increasing trend (from 391.6 KPa to 987.6 KPa after curing for 28 days). By establishing the relationship between compressive strength/flowability and the water–cement ratio, the optimal material ratio is determined to be 9% cement content and 40–43% water content. The engineering application results indicate that the use of CLSM can achieve efficient and high-quality backfilling effects for pipeline trenches. The findings of this research may provide a reference for the application of CLSM in fields with similar geological conditions.

Inspired by the empirical mode decomposition (EMD)-enhanced gas detection work, this paper develops a further improved signal reconstruction method (namely EWT-ASG) for the demodulated harmonics of tunable diode laser absorption spectroscopy (TDLAS), which is mainly based on empirical wavelet transform (EWT) and Savitzky-Golay (S-G) filtering. First, the imported EWT performs better on the decomposition precision as it successfully bypasses the mode aliasing problem of EMD resulting by the lack of mathematical basis. Second, the improved S-G filter effectively suppresses the noisy components of the wavelet coefficients by updating its one key parameter (i.e., window size w) dynamically according to the correlation coefficients between the raw signals and the decomposed wavelet coefficients. The EWT-ASG scheme was first applied on the oxygen concentration detection for pharmaceutical glass vials. The preliminary experimental results indicate that the EWT-ASG method performs better than recent state-of-the-arts, with an average correct discrimination rate of 98.14% when the normalized SNR is 1. Even when the normalized SNR is degenerated from 1 to 0.85, our detection system still survived well, with a highest average correct discrimination rate of 90.45%. The detection system precision is also improved to a large extent, with a minimal Allan deviation of 0.856@(117s).

Due to its advantages of small size and light weight, multi-pulse rectifier has a broad application prospect in the field of aviation. The 18-pulse rectifier is more popular in practical engineering application because of its better rectifier characteristics than 12-pulse, its simpler structure process and easier to realize than 24-pulse. The theoretical research and engineering application of the 18-pulse rectifier have achieved some success. However, whether the reliability of the system can be satisfied has always been one of the key problems to be solved in practical engineering. The aim of this work is to study the reliability modeling and prediction of the 18-pulse rectifier power supply. Its reliability block diagram and mathematical model are established. According to US Military handbook MIL-HDBK-217F, the most authoritative and widely used in the world, the two most commonly used methods for reliability prediction are parts count method and part stress analysis method. The reliability data are estimated by these two methods. It is concluded that the value of mean time between failures in airborne environment is 23326 h, which meets the requirement of 1.5 times of 15000 h. According to the reliability data, the failure mode and effect analysis of its weak links are carried out, and the improvement measures are proposed. This will promote and guide the reliability growth and engineering popularization of the 18-pulse rectifier power supply. At the same time, the reliability of the actual project can be extended to other related engineering applications to ensure the reliable operation of the system.

Developments in defect descriptors and computer vision-based algorithms for automatic optical inspection (AOI) allows for further development in image-based measurements. Defect classification is a vital part of an optical-imaging-based surface quality measuring instrument. The high-speed production rhythm of hot continuous rolling requires an ultra-rapid response to every component as well as algorithms in AOI instrument. In this paper, a simple, fast, yet robust texture descriptor, namely selectively dominant local binary patterns (SDLBPs), is proposed for defect classification. First, an intelligent searching algorithm with a quantitative thresholding mechanism is built to excavate the dominant non-uniform patterns (DNUPs). Second, two convertible schemes of pattern code mapping are developed for binary encoding of all uniform patterns and DNUPs. Third, feature extraction is carried out under SDLBP framework. Finally, an adaptive region weighting method is built for further strengthening the original nearest neighbor classifier in the feature matching stage. The extensive experiments carried out on an open texture database (Outex) and an actual surface defect database (Dragon) indicates that our proposed SDLBP yields promising performance on both classification accuracy and time efficiency.

Time-efficient fault analysis and diagnosis of analog circuits are the most important prerequisites to achieve online health monitoring of electronic equipments, which are involving continuing challenges of ultra-large-scale integration, component tolerance, limited test points but multiple faults. This work reports an FPGA (field programmable gate array)-based analog fault diagnostic system by applying two-dimensional information fusion, two-port network analysis and interval math theory. The proposed system has three advantages over traditional ones. First, it possesses high processing speed and smart circuit size as the embedded algorithms execute parallel on FPGA. Second, the hardware structure has a good compatibility with other diagnostic algorithms. Third, the equipped Ethernet interface enhances its flexibility for remote monitoring and controlling. The experimental results obtained from two realistic example circuits indicate that the proposed methodology had yielded competitive performance in both diagnosis accuracy and time-effectiveness, with about 96% accuracy while within 60 ms computational time.

Solid-state disks (SSDs) tend to replace traditional motor-driven hard disks in high-end storage devices in past few decades. However, various inherent features, such as out-of-place update [resorting to garbage collection (GC)] and limited endurance (resorting to wear leveling), need to be reduced to a large extent before that day comes. Both the GC and wear leveling fundamentally depend on hot data identification (HDI). In this paper, we propose a hot data-aware flash translation layer architecture based on a dynamic virtual page (DVPFTL) so as to improve the performance and lifetime of nand flash devices. First, we develop a generalized dual layer HDI (DL-HDI) framework, which is composed of a cold data preclassifier and a hot data post-identifier. Those can efficiently follow the frequency and recency of information access. Then, we design an adaptive parallelism manager (APM) to assign the clustered data chunks to distinct resident blocks in the SSD so as to prolong its endurance. Finally, the experimental results from our realized SSD prototype indicate that the DVPFTL scheme has reliably improved the parallelizability and endurance of nand flash devices with improved GC-costs, compared with related works.

Accurately evaluating channel state information (CSI) is an extremely important precondition for wireless communication to effectively obtain exact sending data. In order to fast obtain CSI, channel prediction tends to get its popularity in obtaining CSI as a result of the advantages of lightweight calculation and negligible feedback delay. In this paper, a jointly optimized extreme learning machine (JOELM) is proposed for the short-term prediction of fading channel. The JOELM scheme consists of two key steps: intelligent optimization and targeted repair. First, in order to gain high prediction accuracy, the firefly algorithm is imported to intelligently optimize the traditional extreme learning machine. Second, the Savitzky-Golay filter is innovatively adopted for reducing potential prediction errors. Extensive experiments about computational complexity, influences of repair coefficient and weight coefficient, contributing degree of two key steps, amplitude, transmission bit/symbol error rates, root-mean-square errors, and four typical statistical properties are given in final simulation section. The analyzed results indicate that the proposed JOELM can more accurately and efficiently deal with the short-term prediction of fading channel.

Steel strip acts as a fundamental material for the steel industry. Surface defects threaten the steel quality and cause substantial economic and reputation losses. Roll marks, always occurring periodically in a large area, are put on the top of the list of the most serious defects by steel mills. Essentially, the online roll mark detection is a tiny target inspection task in high-resolution images captured under harsh environment. In this paper, a novel method—namely, Smoothing Complete Feature Pyramid Networks (SCFPN)—is proposed for the above focused task. In particular, the concept of complete intersection over union (CIoU) is applied in feature pyramid networks to obtain faster fitting speed and higher prediction accuracy by suppressing the vanishing gradient in training process. Furthermore, label smoothing is employed to promote the generalization ability of model. In view of lack of public surface image database of steel strips, a raw defect database of hot-rolled steel strip surface, CSU_STEEL, is opened for the first time. Experiments on two public databases (DeepPCB and NEU) and one fresh texture database (CSU_STEEL) indicate that our SCFPN yields more competitive results than several prestigious networks—including Faster R-CNN, SSD, YOLOv3, YOLOv4, FPN, DIN, DDN, and CFPN.

With the development of radar power supply system, the power density of DC/DC power supply is becoming larger and larger, but the volume is becoming smaller and smaller, which makes the power density of the power supply increase, and the combination of power modules needs to be optimised to realise the power supply of the whole system. Here, a modular design scheme is introduced, which adopts a three-layer combined structure, and finally realises the DC/DC combined power supply with a total power of 15.6 kW in the total height of 105 mm. The heat design of the power supply is reasonable, the rectangular liquid cooling channel is arranged inside the cooling plate of the chassis, the rectangular heat exchanger fin is added inside the liquid cooling channel with concentrated heat source, and the series-parallel technology is used in the liquid cooling channel inside the plate. The series reliability prediction model can meet the requirements of radar overall design. The project is difficult to implement, and more precise process is needed in practical engineering to strictly control it. The successful implementation of this scheme provides a reference for the realisation of similar radar products.

The computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical industry. The high standard of planar surface quality in the metal manufacturing industry requires that the performance of an automated visual inspection system and its algorithms are constantly improved. This paper attempts to present a comprehensive survey on both two-dimensional and three-dimensional surface defect detection technologies based on reviewing over 160 publications for some typical metal planar material products of steel, aluminum, copper plates and strips. According to the algorithm properties as well as the image features, the existing two-dimensional methodologies are categorized into four groups: statistical, spectral, model, and machine learning-based methods. On the basis of three-dimensional data acquisition, the three-dimensional technologies are divided into stereoscopic vision, photometric stereo, laser scanner, and structured light measurement methods. These classical algorithms and emerging methods are introduced, analyzed, and compared in this review. Finally, the remaining challenges and future research trends of visual defect detection are discussed and forecasted at an abstract level.

Aiming at the problem of large magnitude and high frequency of common-mode voltage (CMV) when space vector pulse width modulation (SVPWM) is used in a three-phase motor fed by a two-level voltage source inverter, a common-mode reduction SVPWM (CMRSVPWM) is studied. In this method, six new sectors are obtained by rotating six sectors of conventional SVPWM by 30°. In odd-numbered sectors, only three non-zero vectors with odd subscripts are used for synthesis, while in even-numbered sectors, only three non-zero vectors with even subscripts are used for synthesis. The actuation durations of three non-zero vectors in each switching period in each sector are given. Simulation and experimental results show that, compared with the conventional SVPWM, the CMV magnitude of CMRSVPWM is reduced by 66.67% and the CMV frequency of CMRSVPWM is reduced from the original switching frequency to the triple fundamental frequency. At the same time, the current, torque and speed of the motor are still good.

Dual Y shift 30° six-phase motors are expected to be extensively applied in high-power yet energy-effective fields, and a harmonic-suppressing control strategy plays a vital role in extending their prominent features of low losses and ultra-quiet operation. Aiming at the suppression of harmonic voltages, this paper proposes a six-phase space vector pulse width modulation method based on an optimization model, namely OM-SVPWM. First, four adjacent large vectors are employed in each of 12 sectors on a fundamental sub-plane. Second, the optimization model is constructed to intelligently determine activation durations of the four vectors, where its objective function aims to minimize the synthesis result on a harmonic sub-plane, and its constraint condition is that the synthesis result on the fundamental sub-plane satisfies a reference vector. Finally, to meet the real-time requirement, optimum solutions are obtained by using general central path following algorithm (GCPFA). Simulation and experiment results prove that, the OM-SVPWM performs around 37% better than a state-of-the-art competitive SVPWM in terms of harmonics suppression, which promise the proposed OM-SVPWM conforms to the energy-effective direction in actual engineering applications.