Juyoung Park^1,*, Jung Hee Lee¹, Junseong Bang^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 965-976, 2021, DOI:10.32604/cmes.2021.014669

Abstract

We propose a mobile system, called PotholeEye⁺, for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video. PotholeEye⁺ pre-processes the images, extracts features, and classifies the distress into a variety of types, while the road manager is driving. Every day for a year, we have tested PotholeEye⁺ on real highway involving real settings, a camera, a mini computer, a GPS receiver, and so on. Consequently, PotholeEye⁺ detected the pavement distress with accuracy of 92%, precision of 87% and recall 74% averagely during driving at an average speed of 110 km/h… More >

Hua Zhang^#, Zhongmin Yang^#, Xilong You, Youqiang Heng, Yan Wang^*

Phyton-International Journal of Experimental Botany, Vol.90, No.2, pp. 389-402, 2021, DOI:10.32604/phyton.2021.014156

Abstract Potassium (K⁺) is a necessary nutrient for plant growth and crop production. The K⁺ transporter plays crucial roles in the absorption and transport of K⁺ in plants. Most K⁺ transporters in Arabidopsis have been reported, but AtKUP12, which is a member of the KT/KUP/HAK family, has not yet been the subject of relevant in-depth research. In the present study, we demonstrated that AtKUP12 plays a crucial role in K⁺ uptake in Arabidopsis under 100 μM lowK⁺ and 125 mM salt stress conditions. AtKUP12 transcripts were induced by K⁺ deficiency and salt stress. We analyzed the K⁺ uptake of AtKUP12 using… More >

Qihang Wang, Gang Wu^*

Computer Systems Science and Engineering, Vol.36, No.1, pp. 225-237, 2021, DOI:10.32604/csse.2021.014100

Abstract AI approaches have been introduced to predict the remaining useful life (RUL) of a machine in modern industrial areas. To apply them well, challenges regarding the high dimension of the data space and noisy data should be met to improve model efficiency and accuracy. In this study, we propose an end-to-end model, termed ACB, for RUL predictions; it combines an autoencoder, convolutional neural network (CNN), and bidirectional long short-term memory. A new penalized root mean square error loss function is included to avoid an overestimation of the RUL. With the CNN-based autoencoder, a high-dimensional data space can be mapped into… More >

Marius Becherer^∗, Michael Zipperle^†, Achim Karduck^‡

Computer Systems Science and Engineering, Vol.35, No.2, pp. 81-89, 2020, DOI:10.32604/csse.2020.35.081

Abstract Machines are serviced too often or only when they fail. This can result in high costs for maintenance and machine failure. The trend of Industry 4.0 and the networking of machines opens up new possibilities for maintenance. Intelligent machines provide data that can be used to predict the ideal time of maintenance. There are different approaches to create a forecast. Depending on the method used, appropriate conditions must be created to improve the forecast. In this paper, results are compiled to give a state of the art of predictive maintenance. First, the different types of maintenance and economic relationships are… More >

Liang Zhao^1,2,3, Wenshun Wang², Wei Zhang¹

Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 705-713, 2019, DOI:10.31209/2019.100000074

Abstract The energy consumption in operation and maintenance stage of construction project accounts for 80% of the whole project life cycle and therefore research on influence factors of energy consumption in project operation and maintenance stage is of great practical significance. Based on data of 260 valid questionnaires in Jiangsu area, structural equation model is adopted in the Thesis for empirical research on influence factors in operation and maintenance stage. Based on theoretical analysis and factor analysis, the conceptual model and research hypothesis for influence factors of energy consumption in operation and maintenance stage are proposed to establish structural equation model… More >

Chien-Jung Lin¹, Eric Novak¹, Michael W. Rich¹, Joseph J. Billadello^1,2

Congenital Heart Disease, Vol.13, No.3, pp. 384-391, 2018, DOI:10.1111/chd.12582

Abstract Background: Adults with congenital heart disease (ACHD) have traditionally been viewed as an underinsured population. Whether this is true in the Affordable Care Act era is unknown. We determined insurance patterns in ACHD patients compared to the non-ACHD cardiology population in a contemporary cohort.
Methods: All cardiology outpatient visits between July 2016 and February 2017 to a large referral center in the United States were reviewed. The primary payer was categorized as health maintenance organization (HMO), preferred provider organization (PPO), Medicare, Medicaid, self-pay, or other. Diagnosis and lesion severity of ACHD were extracted from ICD-10 diagnostic codes and assigned according… More >

Youqin Huang¹, Jiayong Li¹, Jiyang Fu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 845-875, 2019, DOI:10.32604/cmes.2019.07653

Abstract In last few years, big data and deep learning technologies have been successfully applied in various fields of civil engineering with the great progress of machine learning techniques. However, until now, there has been no comprehensive review on its applications in civil engineering. To fill this gap, this paper reviews the application and development of artificial intelligence in civil engineering in recent years, including intelligent algorithms, big data and deep learning. Through the work of this paper, the research direction and difficulties of artificial intelligence in civil engineering for the past few years can be known. It is shown that… More >

J.K. Mohanty¹, A. Adarsh², P.R. Dash¹, K. Parida¹, P.K. Pradhan^1,*

Sound & Vibration, Vol.53, No.5, pp. 223-235, 2019, DOI:10.32604/sv.2019.07737

Abstract Condition monitoring is implementation of the advanced diagnostic techniques to reduce downtime and to increase the efficiency and reliability. The research is for determining the usage of advanced techniques like Vibration analysis, Oil analysis and Thermography to diagnose ensuing problems of the Plant and Machinery at an early stage and plan to take corrective and preventive actions to eliminate the forthcoming breakdown and enhancing the reliability of the system. Nowadays, the most of the industries have adopted the condition monitoring techniques as a part of support system to the basic maintenance strategies. Major condition monitoring technique they follow is Vibration… More >

Gil SP¹, ME Reyna¹, L Seisdedos¹, IP Argüello²

Phyton-International Journal of Experimental Botany, Vol.86, pp. 340-344, 2017, DOI:10.32604/phyton.2017.86.340

Abstract TThe goals of this study were (1) to determinate the cultural practice of Solidago chilensis native Asteraceae from Argentina which have to be used in low maintenance gardening and (2) to describe germination and seedlings of S. chilensis and its morphology type. Germination assay treatments (3 repetitions/50 fruits/year) were intact non-scarified and scarified fruits (achenies), collected in La Mesada, La Calera, Dpto. Colón (Córdoba, Argentina) between 2011-2014. Statistical tests were performed. 50 rhizome cuttings, obtained from mother plants selected in the field, were cultivated in same conditions of the field-substrate with or without rooting solution, and they were cultivated in… More >

Hyun Kyu Shin¹, Yong Han Ahn², Sang Hyo Lee³, Ha Young Kim^4,*

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 911-928, 2019, DOI:10.32604/cmc.2019.08269

Abstract Compressive strength of concrete is a significant factor to assess building structure health and safety. Therefore, various methods have been developed to evaluate the compressive strength of concrete structures. However, previous methods have several challenges in costly, time-consuming, and unsafety. To address these drawbacks, this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network (DCNN). The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy. The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples.… More >