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Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city

Xiangyang Ye, Jian’e Zuo, Ruohan Li, Yajiao Wang, Lili Gan, Zhonghan Yu, Xiaoqing Hu

Frontiers of Environmental Science & Engineering 2019, Volume 13, Issue 2, doi: 10.1007/s11783-019-1102-y

Abstract:

An image-recognition-based diagnosis system of pipe defect types

Keywords： Sewer pipe defects     Defect diagnosing     Image recognition     Multi-features extraction     Support vector machine    

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Progress on early diagnosing Alzheimer’s disease

Frontiers of Medicine 2024, Volume 18, Issue 3,   Pages 446-464 doi: 10.1007/s11684-023-1047-1

Abstract: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects both cognition and non-cognition functions. The disease follows a continuum, starting with preclinical stages, progressing to mild cognitive and behavioral impairment, ultimately leading to dementia. Early detection of AD is crucial for better diagnosis and more effective treatment. However, the current AD diagnostic tests of biomarkers using cerebrospinal fluid and/or brain imaging are invasive or expensive, and mostly are still not able to detect early disease state. Consequently, there is an urgent need to develop new diagnostic techniques with higher sensitivity and specificity during the preclinical stages of AD. Various non-cognitive manifestations, including behavioral abnormalities, sleep disturbances, sensory dysfunctions, and physical changes, have been observed in the preclinical AD stage before occurrence of notable cognitive decline. Recent research advances have identified several biofluid biomarkers as early indicators of AD. This review focuses on these non-cognitive changes and newly discovered biomarkers in AD, specifically addressing the preclinical stages of the disease. Furthermore, it is of importance to explore the potential for developing a predictive system or network to forecast disease onset and progression at the early stage of AD.

Keywords： Alzheimer’s disease     early diagnosis     non-cognitive symptoms     biomarkers    

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Research progress of defect-engineered UiO-66(Zr) MOFs for photocatalytic hydrogen production

Frontiers in Energy 2021, Volume 15, Issue 3,   Pages 656-666 doi: 10.1007/s11708-021-0765-9

Abstract: In recent years, defect-engineered Zr-based UiO-66 metal-organic frameworks (UiO-66(Zr) metal-organicIt is extremely challenging to introduce defect sites in the synthesis of MOFs to regulate the physicochemicalrecent research results of synthesis methods, characterization technologies, and application fields of defect-engineered

Keywords： defect engineering     metal-organic frameworks     UiO-66     photocatalysis    

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Single-electromagnet levitation for density measurement and defect detection

Yuhan JIA, Peng ZHAO, Jun XIE, Xuechun ZHANG, Hongwei ZHOU, Jianzhong FU

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 1,   Pages 186-195 doi: 10.1007/s11465-020-0608-0

Abstract: single-electromagnet levitation device has a large measuring range and can realize accurate density measurement and defect

Keywords： single-electromagnet     electromagnetic levitation     density measurement     defect detection    

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Acoustic fault signal extraction via the line-defect phononic crystals

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 1,   Pages 10-10 doi: 10.1007/s11465-021-0666-y

Abstract: In this paper, 2D line-defect phononic crystals (PCs) consisting of periodic acrylic tubes with slitThe defect band, namely, the formed resonance band of line-defect PCs enables the incident acoustic waveThe effectiveness of weak harmonic and periodic impulse signal detection via line-defect PCs are investigatedAll the numerical and experimental results indicate that line-defect PCs can be well used for extracting

Keywords： phononic crystals     line-defect     fault signal extraction     acoustic enhancement    

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Research on concrete structure defect repair based on three-dimensional printing

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 5,   Pages 731-742 doi: 10.1007/s11709-024-1088-9

Abstract: To enhance the automation level of concrete defect repairs, this study proposes a computer vision-basedFinally, the feasibility of this concrete defect repair method is validated through simulation and experiments

Keywords： concrete     defect detection     3D printing     deep learning     point cloud data    

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The defect-length effect in corrosion detection with magnetic method for bridge cables

Qiwei ZHANG, Rongya XIN

Frontiers of Structural and Civil Engineering 2018, Volume 12, Issue 4,   Pages 662-671 doi: 10.1007/s11709-018-0512-4

Abstract: The magnetic flux (MF) examination shows great potential to detect the corrosion defect, or loss of metallicAn LMA defect in steel cables can be measured accurately when it is longer than a certain length.In this study, the effect of defect length on the MF examination for corrosion detection of bridge cablesAn original analytical model to quantify the influence of defect length is proposed based on the equivalentThen, MF examination experiments are performed on a series of cable models with different defect lengths

Keywords： bridge cable     corrosion detection     defect length     MF examination     quantitative evaluation    

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Statistical analysis of recombination properties of the boron-oxygen defect in p-type Czochralski silicon

Nitin NAMPALLI,Tsun Hang FUNG,Stuart WENHAM,Brett HALLAM,Malcolm ABBOTT

Frontiers in Energy 2017, Volume 11, Issue 1,   Pages 4-22 doi: 10.1007/s11708-016-0442-6

Abstract: lifetime spectroscopy to the study of carrier-induced degradation ascribed to the boron-oxygen (BO) defectresults demonstrate that the capture cross section ratio associated with the donor level of the BO defectFor the data set studied here, it was also found that illumination used to form the defect caused minoralone and is likely related to the acceptor level of the BO defect.While specific to the BO defect, this study has implications for the use of lifetime spectroscopy to

Keywords： Czochralski silicon     boron-oxygen defect     injection dependent lifetime spectroscopy     goodness-of-fit     repeatability    

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VMMAO-YOLO: an ultra-lightweight and scale-aware detector for real-time defect detection of avionics

Frontiers of Mechanical Engineering 2024, Volume 19, Issue 3, doi: 10.1007/s11465-024-0793-3

Abstract: The quality of the exposed avionics solder joints has a significant impact on the stable operation of the in-orbit spacecrafts. Nevertheless, the previously reported inspection methods for multi-scale solder joint defects generally suffer low accuracy and slow detection speed. Herein, a novel real-time detector VMMAO-YOLO is demonstrated based on variable multi-scale concurrency and multi-depth aggregation network (VMMANet) backbone and “one-stop” global information gather-distribute (OS-GD) module. Combined with infrared thermography technology, it can achieve fast and high-precision detection of both internal and external solder joint defects. Specifically, VMMANet is designed for efficient multi-scale feature extraction, which mainly comprises variable multi-scale feature concurrency (VMC) and multi-depth feature aggregation-alignment (MAA) modules. VMC can extract multi-scale features via multiple fix-sized and deformable convolutions, while MAA can aggregate and align multi-depth features on the same order for feature inference. This allows the low-level features with more spatial details to be transmitted in depth-wise, enabling the deeper network to selectively utilize the preceding inference information. The VMMANet replaces inefficient high-density deep convolution by increasing the width of intermediate feature levels, leading to a salient decline in parameters. The OS-GD is developed for efficacious feature extraction, aggregation and distribution, further enhancing the global information gather and deployment capability of the network. On a self-made solder joint image data set, the VMMAO-YOLO achieves a mean average precision mAP@0.5 of 91.6%, surpassing all the mainstream YOLO-series models. Moreover, the VMMAO-YOLO has a body size of merely 19.3 MB and a detection speed up to 119 frame per second, far superior to the prevalent YOLO-series detectors.

Keywords： defect detection of solder joints     VMMAO-YOLO     ultra-lightweight and high-performance     multi-scale feature    

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Road sub-surface defect detection based on gprMax forward simulation-sample generation and Swin Transformer-YOLOX

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 3,   Pages 334-349 doi: 10.1007/s11709-024-1076-0

Abstract: Training samples for deep learning networks are typically obtained through various field experiments, which require significant manpower, resource and time consumption. However, it is possible to utilize simulated data to augment the training samples. In this paper, by comparing the actual experimental model with the simulated model generated by the gprMax [1] forward simulation method, the feasibility of obtaining simulated samples through gprMax simulation is validated. Subsequently, the samples generated by gprMax forward simulation are used for training the network to detect objects in existing real samples. At the same time, aiming at the detection and intelligent recognition of road sub-surface defects, the Swin-YOLOX algorithm is introduced, and the excellence of the detection network, which is improved by augmenting the simulated samples with real samples, is further verified. By comparing the prediction performance of the object detection models, it is observed that the model trained with mixed samples achieved a recall of 94.74% and a mean average precision (mAP) of 97.71%, surpassing the model trained only on real samples by 12.95% and 15.64%, respectively. The feasibility and excellence of training the model with mixed samples are confirmed. The potential of using a fusion of simulated and existing real samples instead of repeatedly acquiring new real samples by field experiment is demonstrated by this study, thereby improving detection efficiency, saving resources, and providing a new approach to the problem of multiple interpretations in ground penetrating radar (GPR) data.

Keywords： ground penetrating radar     gprMax     forward modeling     sample generation     Swin-YOLOX     object detection    

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Identification of a novel variant associated with laterality defects, congenital heart diseases, and sperm defects in humans

Frontiers of Medicine 2024, Volume 18, Issue 3,   Pages 558-564 doi: 10.1007/s11684-023-1042-6

Abstract: The establishment of left–right asymmetry is a fundamental process in animal development. Interference with this process leads to a range of disorders collectively known as laterality defects, which manifest as abnormal arrangements of visceral organs. Among patients with laterality defects, congenital heart diseases (CHD) are prevalent. Through multiple model organisms, extant research has established that myosin-Id (MYO1D) deficiency causes laterality defects. This study investigated over a hundred cases and identified a novel biallelic variant of MYO1D (NM_015194: c.1531G>A; p.D511N) in a consanguineous family with complex CHD and laterality defects. Further examination of the proband revealed asthenoteratozoospermia and shortened sperm. Afterward, the effects of the D511N variant and another known MYO1D variant (NM_015194: c.2293C>T; p.P765S) were assessed. The assessment showed that both enhance the interaction with β-actin and SPAG6. Overall, this study revealed the genetic heterogeneity of this rare disease and found that MYO1D variants are correlated with laterality defects and CHD in humans. Furthermore, this research established a connection between sperm defects and MYO1D variants. It offers guidance for exploring infertility and reproductive health concerns. The findings provide a critical basis for advancing personalized medicine and genetic counseling.

Keywords： MYO1D     laterality defect     congenital heart disease     sperm defect     β-actin     SPAG6    

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Study on Defect Detection Technology for Bridge Deck Pavements

Guo Chengchao,Xu Pengfei and Cui Can

Strategic Study of CAE 2017, Volume 19, Issue 6,   Pages 38-43 doi: 10.15302/J-SSCAE-2017.06.006

Abstract:

The bridge deck is the most vulnerable part of a bridge during its entire life cycle. The deck pavement is often adversely affected by improper construction methods, the aging of concrete, and corrosion of steel bars. This then has an adverse effect on the structure and overall performance of the bridge. Therefore, it is necessary to determined defects related to the bridge deck and to conduct relevant quality evaluations. This article describes the mechanism, application methods, and testing equipment of four mainstream nondestructive testing technologies used worldwide: ground penetrating radar, half-cell potential, impact echo, and infrared thermography. The use of one or more of these methods can accurately assess the deterioration of the bridge deck and make a rapid, nondestructive evaluation, which provides technical support for rapid detection and accurate evaluation of the deck situation, leading to reduced structural defects and an extended bridge life cycle.

Keywords： bridge deck pavement     defect     nondestructive testing    

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Effect of cavity defect on the triaxial mechanical properties of high-performance concrete

Yanbin ZHANG; Zhe WANG; Mingyu FENG

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 5,   Pages 600-614 doi: 10.1007/s11709-022-0821-5

Abstract: The stress concentration of pipe structure or cavity defect has a great effect on the mechanical properties

Keywords： high-performance concrete     cavity     conventional triaxial compression     pressurized liquid     modified power-law criterion    

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Construction of defect-containing UiO-66/MoSe heterojunctions with superior photocatalytic performance

Frontiers of Chemical Science and Engineering 2023, Volume 17, Issue 4,   Pages 449-459 doi: 10.1007/s11705-022-2226-3

Abstract: Metal–organic frameworks are recognized as promising multifunctional materials, especially metal–organic framework-based photocatalysts, which are considered to be ideal photocatalytic materials. Herein, a new type of UiO-66/MoSe2 composite was prepared using the solvothermal method. The optimum composite was selected by adjusting the mass ratio of UiO-66 and MoSe2. X-ray diffraction analysis showed that the mass ratio influenced the crystal plane exposure rate of the composite, which may have affected its photocatalytic performance. The composite is composed of ultra-thin flower-like MoSe2 that wrapped around cubic UiO-66, a structure that increases the abundance of active sites for reactions and is more conducive to the separation of carriers. The photocatalytic properties of the composite were evaluated by measuring the degradation rate of Rhodamine B and the catalyst’s ability to reduce Cr(VI)-containing wastewater under visible light irradiation. Rhodamine B was decolorized completely in 120 min, and most of the Cr(VI) was reduced within 150 min. The photochemical mechanism of the complex was studied in detail. The existence of Mo6+ and oxygen vacancies, in addition to the Z-type heterojunction promote the separation of electrons and holes, which enhances the photocatalytic effect.

Keywords： UiO-66/MoSe2     photocatalysis     dye-containing wastewater     heavy metal wastewater     oxygen vacancies    

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Afeature selection approach based on a similarity measure for software defect prediction Article

Qiao YU, Shu-juan JIANG, Rong-cun WANG, Hong-yang WANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1744-1753 doi: 10.1631/FITEE.1601322

Abstract: Software defect prediction is aimed to find potential defects based on historical data and software featuresthe class, we present a feature selection approach based on a similarity measure (SM) for software defect

Keywords： Software defect prediction     Feature selection     Similarity measure     Feature weights     Feature ranking list    

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Title Author Date Type Operation

Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city

Xiangyang Ye, Jian’e Zuo, Ruohan Li, Yajiao Wang, Lili Gan, Zhonghan Yu, Xiaoqing Hu

Journal Article

Progress on early diagnosing Alzheimer’s disease

Journal Article

Research progress of defect-engineered UiO-66(Zr) MOFs for photocatalytic hydrogen production

Journal Article

Single-electromagnet levitation for density measurement and defect detection

Yuhan JIA, Peng ZHAO, Jun XIE, Xuechun ZHANG, Hongwei ZHOU, Jianzhong FU

Journal Article

Acoustic fault signal extraction via the line-defect phononic crystals

Journal Article

Research on concrete structure defect repair based on three-dimensional printing

Journal Article

The defect-length effect in corrosion detection with magnetic method for bridge cables

Qiwei ZHANG, Rongya XIN

Journal Article

Statistical analysis of recombination properties of the boron-oxygen defect in p-type Czochralski silicon

Nitin NAMPALLI,Tsun Hang FUNG,Stuart WENHAM,Brett HALLAM,Malcolm ABBOTT

Journal Article

VMMAO-YOLO: an ultra-lightweight and scale-aware detector for real-time defect detection of avionics

Journal Article

Road sub-surface defect detection based on gprMax forward simulation-sample generation and Swin Transformer-YOLOX

Journal Article

Identification of a novel variant associated with laterality defects, congenital heart diseases, and sperm defects in humans

Journal Article

Study on Defect Detection Technology for Bridge Deck Pavements

Guo Chengchao,Xu Pengfei and Cui Can

Journal Article

Effect of cavity defect on the triaxial mechanical properties of high-performance concrete

Yanbin ZHANG; Zhe WANG; Mingyu FENG

Journal Article

Construction of defect-containing UiO-66/MoSe heterojunctions with superior photocatalytic performance

Journal Article

Afeature selection approach based on a similarity measure for software defect prediction

Qiao YU, Shu-juan JIANG, Rong-cun WANG, Hong-yang WANG

Journal Article