bluuebunny/crossref_metadata_2025 huggingface dataset:
Applications of Artificial Intelligence to Photovoltaic Systems: A Review
Authors: Mateo Romero Héctor Felipe , González Rebollo Miguel Ángel , Cardeñoso-Payo Valentín , Alonso Gómez Victor , Redondo Plaza Alberto , Moyo Ranganai Tawanda , Hernández-Callejo Luis
Published: October 2022

This article analyzes the relationship between artificial intelligence (AI) and photovoltaic (PV) systems. Solar energy is one of the most important renewable energies, and the investment of businesses and governments is increasing every year. AI is used to solve the most important problems found in PV systems, such as the tracking of the Max Power Point of the PV modules, the forecasting of the energy produced by the PV system, the estimation of the parameters of the equivalent model of PV modules or the detection of faults found in PV modules or cells. AI techniques perform better than classical approaches, even though they have some limitations such as the amount of data and the high computation times needed for performing the training. Research is still being conducted in order to solve these problems and find techniques with better performance. This article analyzes the most relevant scientific works that use artificial intelligence to deal with the key PV problems by searching terms related with artificial intelligence and photovoltaic systems in the most important academic research databases. The number of publications shows that this field is of great interest to researchers. The findings also show that these kinds of algorithms really have helped to solve these issues or to improve the previous solutions in terms of efficiency or accuracy.
Lemurs Optimizer: A New Metaheuristic Algorithm for Global Optimization
Authors: Abasi Ammar Kamal , Makhadmeh Sharif Naser , Al-Betar Mohammed Azmi , Alomari Osama Ahmad , Awadallah Mohammed A. , Alyasseri Zaid Abdi Alkareem , Doush Iyad Abu , Elnagar Ashraf , Alkhammash Eman H. , Hadjouni Myriam
Published: October 2022

The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm’s primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle local search, exploitation, and exploration search concepts. The LO is first benchmarked on twenty-three standard optimization functions. Additionally, the LO is used to solve three real-world problems to evaluate its performance and effectiveness. In this direction, LO is compared to six well-known algorithms: Salp Swarm Algorithm (SSA), Artificial Bee Colony (ABC), Sine Cosine Algorithm (SCA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), and JAYA algorithm. The findings show that the proposed algorithm outperforms these algorithms in fourteen standard optimization functions and proves the LO’s robust performance in managing its exploration and exploitation capabilities, which significantly leads LO towards the global optimum. The real-world experimental findings demonstrate how LO may tackle such challenges competitively.
Bio-Inspired Model-Based Design and Control of Bipedal Robot
Authors: Polakovič David , Juhás Martin , Juhásová Bohuslava , Červeňanská Zuzana
Published: October 2022

The article aims to describe stages of development of a mobile, anthropomorphic robot. The individual phases of the bio-inspired model-based design of a two-legged robot are described, from the analysis of human walking patterns, through the creation of a simulation model and a control system to the creation and testing the physical model of such a robot. The bio-inspired design is based on an analysis of the movements of the individual parts of a lower human body during walking locomotion. Based on this analysis, the chosen motion model is described as a combination of passive and dynamic walking with a controlled, linear inverted pendulum model. When creating the simulation model, an open kinematic chain consisting of a base (the frame) and two effectors (the legs) is used. The simulation of the virtual model is realized using the software tool Matlab with its toolboxes Simulink and Simscape, and the results confirm the correctness of the design. The feasibility of the design is confirmed by creation of a physical robot skeleton using 3D printing and by a commissioning of the control system based on Atmel ATmega2560 and Raspberry Pi Zero W microcontrollers.
Sustainable Manufacturing Decisions through the Optimization of Printing Parameters in 3D Printing
Authors: Yang Cheng-Jung , Wu Sin-Syuan
Published: October 2022

The 3D printers integrated with fused filament fabrication (FFF) are highly valued worldwide because of their properties, which include fast proofing, compatibility with various materials, and low printing cost. The competitiveness of FFF can be enhanced by improving printing quality. However, due to the increasing sustainability issues worldwide, there is an urgent need to lower energy consumption. In this study, we focused on fan rate, printing speed, nozzle temperature, build plate temperature, and layer thickness as factors that directly impact the dimensional accuracy, carbon dioxide emissions, and printing cost of FFF printers. Several single-objective and multiobjective optimization tasks were performed using the Taguchi method and desirability approach to implement sustainable manufacturing decisions. In single-objective optimization, the inner width, outer width, material cost, and labor cost were most easily affected by the layer thickness. The outer length, carbon dioxide emissions, and electricity cost were significantly affected by the build plate temperature. In multiobjective optimization, a different set of printing parameters can be used to optimize dimensional accuracy, carbon dioxide emissions, material cost, labor cost, and electricity cost. This study helps users to obtain optimal solutions under different optimization requirements to cope with diverse manufacturing characteristics.
Ultra-Compact Digital Metasurface Polarization Beam Splitter via Physics-Constrained Inverse Design
Authors: Shi Hao , Wang Lin , Zhang Lei , Wu Yanqing , Li Zhenjiang , Wang Lu , Tai Renzhong
Published: October 2022

Inverse design effectively promotes the miniaturization of integrated photonic devices through the modulation of subwavelength structures. Utilizing a theoretical prior based inverse design, we propose an ultra-compact integrated polarizing beam splitter consisting of a standard silicon-on-insulator (SOI) substrate and a tunable air–silicon column two-dimensional code metasurface, with a footprint of 5 × 2.7 μm2. The effective refractive index of the waveguide is modulated by adjusting the two-dimensional code morphology in the additional layer to achieve efficient polarization beam splitting. The simulation results demonstrate high performance, with a low insertion loss (<0.87 dB) and high extinction ratio (>10.76 dB) in a bandwidth of 80 nm covering the C-band. The device can withstand manufacturing errors up to ±20 nm and is robust to process defects, such as the outer proximity effect, and thus is suitable for ultra-compact on-chip optical interconnects.
Emerging Paradigms and Architectures for Industry 5.0 Applications
Authors: Fraga-Lamas Paula , Lopes Sérgio Ivan , Fernández-Caramés Tiago M.
Published: October 2022

After the success of the first Special Issue on “Emerging Paradigms and Architectures for Industry 4 [...]
A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System
Authors: Lee Chung-Hong , Yang Hsin-Chang , Su Xuan-Qi , Tang Yao-Xiang
Published: October 2022

To achieve successful investments, in addition to financial expertise and knowledge of market information, a further critical factor is an individual’s personality. Decisive people tend to be able to quickly judge when to invest, while calm people can analyze the current situation more carefully and make appropriate decisions. Therefore, in this study, we developed a multimodal personality-recognition system to understand investors’ personality traits. The system analyzes the personality traits of investors when they share their investment experiences and plans, allowing them to understand their own personality traits before investing. To perform system functions, we collected digital human behavior data through video-recording devices and extracted human behavior features using video, speech, and text data. We then used data fusion to fuse human behavior features from heterogeneous data to address the problem of learning only one-sided information from a single modality. Through several experiments, we demonstrated that multimodal (i.e., three different signal inputs) personality trait analysis is more accurate than unimodal models. We also used statistical methods and questionnaires to evaluate the correlation between the investor’s personality traits and risk tolerance. It was found that investors with higher openness, extraversion, and lower neuroticism personality traits took higher risks, which is similar to research findings in the field of behavioral finance. Experimental results show that, in a case study, our multimodal personality prediction system exhibits high performance with highly accurate prediction scores in various metrics.
Parametric Quasi-Static Study of the Effect of Misalignments on the Path of Contact, Transmission Error, and Contact Pressure of Crowned Spur and Helical Gear Teeth Using a Novel Rapidly Convergent Method
Authors: Temirkhan Maksat , Tariq Hamza Bin , Kaloudis Konstantinos , Kalligeros Christos , Spitas Vasilios , Spitas Christos
Published: October 2022

Quasi-static modelling of non-conjugate contact of tooth-modified spur and helical gears has been studied at length, but existing models are hindered by convergence problems and require a brute-force numerical approach. Here, a novel, computationally efficient, and stable and unconditionally convergent model is developed for non-conjugate tooth contact in three dimensions and applied to crowned spur and helical gears to assess parametrically the sensitivity of various in- and out-of-plane misalignments on the path of contact, transmission error, and contact pressure. Performance metrics are defined, and comparisons are made between three different crowning modification functions.
Investigating the Influence of the Diffraction Effect on Fourier Transform Spectroscopy with Bandpass Sampling
Authors: Chen Xinwen , Lv Qunbo , Tang Yinhui , Wang Jianwei , Zhao Na , Tan Zheng , Li Weiyan , Liu Yangyang , Si Jia , Xiangli Bin
Published: October 2022

Fourier transform spectroscopy with bandpass sampling (BPS-FTS) increases the interferogram sampling step and reduces sampling frequency, thereby reducing system implementation difficulty. However, this technology has only been introduced in principle and lacks analysis of the actual system error. In this paper, we present a theoretical analysis of the optical system diffraction effect, derive the bandpass spectrum bandwidth and interferogram sampling step under the influence of the diffraction effect, and conduct modeling and a simulation analysis with and without the diffraction effect. Through simulation, we found that when the diffraction effect was considered, the relative deviation of the recovered spectrum from the input spectrum was 1.30%, and the spectral similarity was 0.9669. However, without considering the diffraction effect, the relative deviation of the recovered spectrum from the input spectrum was 6.23%, and the spectral similarity was only −0.0056. The simulation results demonstrate that the diffraction effect has a significant influence on BPS-FTS interferogram sampling and recovery spectra. The results of this study have reference significance for their technical realization of BPS-FTS.
Research on the Method of Temporary Prestressing to Regulate the Stress in the Section during the Construction of the Main Arch Ring of the Cantilever Cast Arch Bridge
Authors: Tian Zhongchu , Zhang Zujun , Peng Wenping , Dai Ye , Cai Yue , Xu Binlin
Published: October 2022

To control the level of tensile stress in the section of reinforced concrete arch cantilever construction, a construction control method of temporary prestressing reinforcement is proposed in this study. The influence law of temporary prestressing on the arch ring section stress is revealed based on the engineering background (Shatuo Special Bridge in Guizhou). Meanwhile, a temporary prestressing test is carried out on the ring section of the main arch to verify the effectiveness of the method. The results show that the prestressing configuration during the construction of the main arch ring of the suspension arch bridge has obvious influence on the control of tensile stress. The method can also effectively improve the force uniformity of the buckle cables, reduce the variation range of cable forces during construction, and improve the safety of arch ring construction.
Electric Motor Control for Online Tuning Based on Positive Flow System for Electric Construction Machinery
Authors: Li Zhongshen , Lin Tianliang , Zhao Yi , Chen Qihuai , Fu Shengjie , Ren Haoling , Gong Wen
Published: October 2022

With the increasingly serious environmental pollution and stricter emission standards, energy saving and emission reduction in construction machinery (CM) are imminent. Electrification of CM is an inevitable trend. Due to the complicated working conditions and the violent load fluctuations, the existing motor controller cannot adapt well to the operating characteristics of the electric CM. In this paper, the work requirements of the positive flow system are analyzed. Considering the robustness and resistance to load disturbance of the control system, a vector control strategy through online tuning of speed PI parameters based on fuzzy control is proposed. The characteristics of the control system are analyzed with the co-simulation of AMESim and MATLAB/Simulink. A test rig was set up to verify the feasibility of the control strategy. The results show that when the load is variable, the fluctuation of rotation speed is approximately 1.5% with fuzzy PI, and 2.5% with traditional PI. Furtherly, the vector control algorithm based on fuzzy PI is tested on a positive flow system. The test results show that when the load fluctuates drastically, the electric motor speed fluctuation is within 3.5%, and the steady-state error is only approximately 0.3%.
Software-Defined Network-Based Energy-Aware Routing Method for Wireless Sensor Networks in Industry 4.0
Authors: Almuntasheri Sumayah , Alenazi Mohammed J. F.
Published: October 2022

Recent technological developments have led to the emergence of the next generation of industry—Industry 4.0. The Industrial Internet of Things (IIoT) is a key enabler of this new manufacturing paradigm where millions of interconnected smart devices, such as sensors and robots, manage massive amounts of data. Wireless sensor networks (WSNs), which allow the integration, flexibility, and scalability of the production line, thus avoiding the need for complex and expensive wired networks, are essential for IIoT. Nevertheless, the nonstop improvements of the smart industry have increased the amount of data transmitted by WSNs, making their nodes, which rely on small batteries, prone to exhaustion. In this scenario, where the transmission could be abruptly interrupted, losing time, information, and money, the development of energy-based management strategies for reducing the energy consumption of WSNs is urgent. In this paper, a software-defined network (SDN)-based energy-aware routing protocol is proposed to optimize the power consumption of WSNs within the framework of IIoT to support Industry 4.0. The SDN controller estimates the energy level of critical nodes in the WSN and decides the best routing path based on their energy consumption rather than on the widely used shortest-path criterion. Experimental results, obtained via a Mininet-Wifi simulation, show that the proposed approach prevents WSNs’ nodes from draining their batteries and abruptly interrupting the data transmission. Hence, valuable retransmission time is saved, potential information loss is prevented, the need for replacing the node’s battery is avoided, and the transmission lifetime is prolonged. In addition, the baseline shortest-path routing method is outperformed in terms of energy consumption and node failure, doubling its transmission time.
Research on Traditional Mongolian-Chinese Neural Machine Translation Based on Dependency Syntactic Information and Transformer Model
Authors: Qing-dao-er-ji Ren , Cheng Kun , Pang Rui
Published: October 2022

Neural machine translation (NMT) is a data-driven machine translation approach that has proven its superiority in large corpora, but it still has much room for improvement when the corpus resources are not abundant. This work aims to improve the translation quality of Traditional Mongolian-Chinese (MN-CH). First, the baseline model is constructed based on the Transformer model, and then two different syntax-assisted learning units are added to the encoder and decoder. Finally, the encoder’s ability to learn Traditional Mongolian syntax is implicitly strengthened, and the knowledge of Chinese-dependent syntax is taken as prior knowledge to explicitly guide the decoder to learn Chinese syntax. The average BLEU values measured under two experimental conditions showed that the proposed improved model improved by 6.706 (45.141–38.435) and 5.409 (41.930–36.521) compared with the baseline model. The analysis of the experimental results also revealed that the proposed improved model was still deficient in learning Chinese syntax, and then the Primer-EZ method was introduced to ameliorate this problem, leading to faster convergence and better translation quality. The final improved model had an average BLEU value increase of 9.113 (45.634–36.521) compared with the baseline model at experimental conditions of N = 5 and epochs = 35. The experiments showed that both the proposed model architecture and prior knowledge could effectively lead to an increase in BLEU value, and the addition of syntactic-assisted learning units not only corrected the initial association but also alleviated the long-term dependence between words.
Machine Learning-Based Forest Burned Area Detection with Various Input Variables: A Case Study of South Korea
Authors: Lee Changhui , Park Seonyoung , Kim Taeheon , Liu Sicong , Md Reba Mohd Nadzri , Oh Jaehong , Han Youkyung
Published: October 2022

Recently, an increase in wildfire incidents has caused significant damage from economical, humanitarian, and environmental perspectives. Wildfires have increased in severity, frequency, and duration because of climate change and rising global temperatures, resulting in the release of massive volumes of greenhouse gases, the destruction of forests and associated habitats, and the damage to infrastructures. Therefore, identifying burned areas is crucial for monitoring wildfire damage. In this study, we aim at detecting forest burned areas occurring in South Korea using optical satellite images. To exploit the advantage of applying machine learning, the present study employs representative three machine learning methods, Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and U-Net, to detect forest burned areas with a combination of input variables, namely Surface Reflectance (SR), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR). Two study sites of recently occurred forest fire events in South Korea were selected, and Sentinel-2 satellite images were used by considering a small scale of the forest fires. The quantitative and qualitative evaluations according to the machine learning methods and input variables were carried out. In terms of the comparison focusing on machine learning models, the U-Net showed the highest accuracy in both sites amongst the designed variants. The pre and post fire images by SR, NDVI, NBR, and difference of indices as the main inputs showed the best result. We also demonstrated that diverse landcovers may result in a poor burned area detection performance by comparing the results of the two sites.
The Ionic Component of PM2.5 May Be Associated with Respiratory Symptoms and Peak Expiratory Flow Rate
Authors: Odajima Hiroshi , Matsuzaki Hiroshi , Akamine Yuko , Kojima Kaoru , Murakami Yoko , Yoshino Ayako , Takami Akinori , Hayakawa Kazuichi , Hara Akinori , Nakamura Hiroyuki
Published: October 2022

(1) Background: Few studies have evaluated the association between the ionic components of PM2.5 and respiratory symptoms or peak expiratory flow rate (PEFR). The aim of this study was to clarify these associations. (2) Methods: Patients with allergic diseases treated at Fukuoka Hospital recorded their morning and evening PEFR and respiratory symptoms in a diary. We measured PM2.5 and its ionic components in Fukuoka City and examined the relationship with PEFR and respiratory symptoms by univariate and multivariate analysis. (3) Results: Among the ionic components of PM2.5, Cl−, NO3−, Na+, K+, and Mg2+ were significantly correlated with the frequency of coughing and nasal symptoms. In univariate analysis, the concentrations of each of the above ions was significantly associated with a decrease in PEFR and the concentrations of each was associated with at least one respiratory symptom or PEFR. Multivariate analysis of items significantly correlated with PEFR indicated that the concentration of ionic components may predict changes in PEFR. (4) Conclusions: In patients with allergic diseases, some ionic components of PM2.5 may increase the frequency of respiratory symptoms and decrease PEFR, so further study and caution are required in daily clinical practice.
Total Mercury (THg) Content in Red Mullet (Mullus barbatus) from Adriatic Sea (Central Mediterranean Sea): Relation to Biological Parameters, Sampling Area and Human Health Risk Assessment
Authors: Girolametti Federico , Frapiccini Emanuela , Annibaldi Anna , Illuminati Silvia , Panfili Monica , Marini Mauro , Santojanni Alberto , Truzzi Cristina
Published: October 2022

Mercury (Hg) is a pollutant that has toxic effects on ecosystems and biota. As it biomagnifies in the food chain, its presence in edible fish poses a high risk to human health. Herein, total Hg (THg) content was quantified in 2018–2019 using thermal decomposition amalgamation atomic absorption spectrometry in muscle tissue of red mullet (Mullus barbatus), a commercially important species throughout the Adriatic Sea (Central Mediterranean Sea). Specimens were grouped into 16 pools based on sex, reproductive stages, and sampling area. The overall mean value of THg content was 0.20 ± 0.15 mg kg−1 in terms of wet weight. THg levels in males and females showed no statistically significant differences, whereas specimens that were captured in open sea showed a higher THg content than coastal samples. Statistically significant differences between THg content and the reproductive stages of fish were found in females. However, neither lipid content nor fish length were statistically correlated with THg content. The analyzed specimens were considered to be safe food according to EU directives, but it is necessary to exercise caution and further investigate Italian people in the 0–18 age group, because they were found to be exposed to a higher dose of methylmercury than the safety threshold set by the EFSA.
Trypsin Inhibitor from Soybean Whey Wastewater: Isolation, Purification and Stability
Authors: Zhang Yihao , Zhang Yinxiao , Ying Zhiwei , Li Wenhui , Li He , Liu Xinqi
Published: October 2022

Soybean trypsin inhibitor (STI) was obtained from simulated soybean whey wastewater through a sustainable method consisting of isoelectric precipitation, ammonium sulfate salting out, and gel filtration chromatography, and the effect of temperature, pH, and pepsin on the stability of STI was also discussed. The results showed that the recovery rate of the trypsin inhibitory activity was 89.47%, the purity and the specific activity of STI were 71.11%, and 1442.5 TIU/mg in the conditions of pH 4.0 and 40% ammonium sulfate saturation. The soybean Kunitz trypsin inhibitor (KTI) and soybean Bowman–Brik trypsin inhibitor (BBI) were obtained via gel filtration chromatography, and their specific activity levels were 1733.5 TIU/mg and 2588.3 TIU/mg, respectively. The STI displayed good stability over a wide temperature and pH range. The STI, KTI, and BBI were all resistant to pepsin hydrolysis, and their ability was ranked as BBI > STI > KTI. These findings will provide a theoretical basis for recycling STI from soybean whey wastewater and promoting better active compound utilization.
Determinants of Collaborative Robots Innovation Adoption in Small and Medium-Sized Enterprises: An Empirical Study in China
Authors: Liu Dong , Cao Junwei
Published: October 2022

With the rapid development of industry 4.0 and the boom of large-scale product customization, the adoption of collaborative robots’ innovation becomes a hot topic in research. Previous studies have mainly focused on individuals, but few on enterprises, and in particular, there has been a lack of empirical research on the enterprise level. Based on the combined model of Technology-Organization-Environment Framework (TOE) and Diffusion of Innovations Theory (DOI), this study investigated 373 small and medium-sized enterprises (SMEs) in Guangdong Province, China, to explore the determinants of SMEs’ adoption of collaborative robot innovation in technology, organization, and environment. The result shows that the technical factors of relative advantage, compatibility, observability, and trialability have a significant positive correlation with the adoption of collaborative robots, while complexity has a significant negative correlation with the adoption. Among the organizational factors, top management support and organizational readiness have a significant positive correlation with the adoption of collaborative robots. Among the environmental factors, agent support is positively and significantly correlated with adoption. The findings will help practitioners develop appropriate strategies for the adoption of collaborative robot innovation.
Hydroxytyrosol-Rich Olive Extract for Plasma Cholesterol Control
Authors: Cicero Arrigo F. G. , Fogacci Federica , Di Micoli Antonio , Veronesi Maddalena , Grandi Elisa , Borghi Claudio
Published: October 2022

Emerging research and epidemiological studies established the health benefits of the Mediterranean diet, whose hallmark is the high consumption of olives and olive oil as the primary source of dietary fatty acids and major sources of antioxidants. The aim of this study was to evaluate the effect of daily dietary supplementation with highly standardized polyphenols—mainly hydroxytyrosol—which are derived from olive oil production by-products of an Italian olive variety (Coratina Olive) on the plasma cholesterol of a sample of hypercholesterolemic individuals. This single-arm, non-controlled, non-randomized, prospective pilot clinical study involved a sample of 30 volunteers with polygenic hypercholesterolemia. The study design included a 2-week run-in and a 4-week intervention period. Patients were evaluated for their clinical status and by the execution of a physical examination and laboratory analyses before and after the treatment. The intervention effect was assessed using Levene’s test followed by the independent Student’s t test after the log-transformation of the non-normally distributed continuous variables. Dietary supplementation with highly standardized polyphenols that are derived from Coratina Olive (namely SelectSIEVE® OptiChol) was associated with a significant improvement in systolic blood pressure, pulse pressure, total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol, non-HDL-C, fasting plasma glucose, and uric acid compared to baseline values. Furthermore, SelectSIEVE® OptiChol was well tolerated by volunteers. We acknowledge that the study has some limitations, namely the small patient sample, the short follow-up, and the lack of randomization and control procedures. However, these results are consistent with previous literature that referred to extracts from different olive varieties. Definitely, our observations lay further foundations for the use of polyphenolic-rich olive extract from Coratina Olive in the prevention and treatment of first-stage metabolic syndrome.
Near-Infrared Image Colorization Using Asymmetric Codec and Pixel-Level Fusion
Authors: Ma Xiaoyu , Huang Wei , Huang Rui , Liu Xuefeng
Published: October 2022

This paper mainly studies the colorization of near-infrared (NIR) images. Image colorization methods cannot be extended to NIR image colorization since the wavelength band of the NIR image exceeds the visible light spectral range and it is often linearly independent of the luminance of the RGB image. Furthermore, a symmetric codec, which cannot guarantee the ability of the encoder to extract features, is often used as the main frame of the network in both CNN-based colorization networks and CycleGAN-based colorization networks. In order to deal with the investigated problem, we propose a novel NIR colorization method using asymmetric codec (ACD) and pixel-level fusion. ACD is designed to improve the feature extraction ability of the encoder by allowing the information to enter deeper into the model and learning more non-redundant information. In addition, the global and local feature fusion networks (GLFFNet) are embedded between the encoder and the decoder to improve the prediction of the subtle color information of the image. The ACD and GLFFNet together constitute the colorization network (ColorNet) in this paper. Bilateral filtering and weighted least squares filtering (BFWLS) are used to fuse the pixel-level information of the input NIR image into the raw output image of the ColorNet. Finally, an intensive comparison analysis based on common datasets is conducted to verify superiority over existing methods in qualitative and quantitative visual assessments.
Investigation into the Modulation Characteristics of Motor Current Signals in a Belt Transmission System for Machining Monitoring
Authors: Zou Zhexiang , Li Chun , Huang Baoshan , Shen Guoji , Gu Fengshou , Ball Andrew D.
Published: October 2022

Belt transmission is one of the most common forms of transmission on CNC lathes. To assess the impact of belt performance on monitoring machining conditions using motor current signal analysis, we developed a dynamic model for belt-driven electromechanical spindle systems. This model enables the coupling of multi-physics variables including mechanical dynamics, particularly the dynamic axil and belt bending motions of belt transmissions and electromagnetic effects. Thus, various changes in belt transmission, including different degrees of belt tension and defects with inherent motor and spindle errors, can be investigated. The modulation characteristics of the current and speed signals revealed that belt transmission can cause a high degree of modulation, reflected by the rich harmonic sidebands at the belt-passing frequency in the motor current spectrum. The rate and extent of belt wear worsened with excessive wear and inadequate tension. It was confirmed that the sideband at the spindle rotating frequency was slightly impacted by changes in the belt conditions. This model was verified based on a CNC lathe induced with different degrees of belt looseness. These conclusions drawn from the numerical analyses can be used for motor current signal analysis, providing reliable and accurate results for machining monitoring.
An Efficient Method for Detecting Asphalt Pavement Cracks and Sealed Cracks Based on a Deep Data-Driven Model
Authors: Yang Nan , Li Yongshang , Ma Ronggui
Published: October 2022

Thanks to the development of deep learning, the use of data-driven methods to detect pavement distresses has become an active research field. This research makes four contributions to address the problem of efficiently detecting cracks and sealed cracks in asphalt pavements. First, a dataset of pavement cracks and sealed cracks is created, which consists of 10,400 images obtained by a vehicle equipped with a highway condition monitor, with 202,840 labeled distress instances included in these pavement images. Second, we develop a dense and redundant crack annotation method based on the characteristics of the crack images. Compared with traditional annotation, the method we propose generates more object instances, and the localization is more accurate. Next, to achieve efficient crack detection, a semi-automatic crack annotation method is proposed, which reduces the working time by 80% compared with fully manual annotation. Finally, comparative experiments are conducted on our dataset using 13 currently prevailing object detection algorithms. The results show that dense and redundant annotation is effective; moreover, cracks and sealed cracks can be efficiently and accurately detected using the YOLOv5 series model and YOLOv5s is the most balanced model with an F1-score of 86.79% and an inference time of 14.8ms. The pavement crack and sealed crack dataset created in this study is publicly available.
No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System
Authors: Hasan Md Mehedi , Islam Md. Ariful , Rahman Sejuti , Frater Michael R. , Arnold John F.
Published: October 2022

Provisioning the stereoscopic 3D (S3D) video transmission services of admissible quality in a wireless environment is an immense challenge for video service providers. Unlike for 2D videos, a widely accepted No-reference objective model for assessing transmitted 3D videos that explores the Human Visual System (HVS) appropriately has not been developed yet. Distortions perceived in 2D and 3D videos are significantly different due to the sophisticated manner in which the HVS handles the dissimilarities between the two different views. In real-time video transmission, viewers only have the distorted or receiver end content of the original video acquired through the communication medium. In this paper, we propose a No-reference quality assessment method that can estimate the quality of a stereoscopic 3D video based on HVS. By evaluating perceptual aspects and correlations of visual binocular impacts in a stereoscopic movie, the approach creates a way for the objective quality measure to assess impairments similarly to a human observer who would experience the similar material. Firstly, the disparity is measured and quantified by the region-based similarity matching algorithm, and then, the magnitude of the edge difference is calculated to delimit the visually perceptible areas of an image. Finally, an objective metric is approximated by extracting these significant perceptual image features. Experimental analysis with standard S3D video datasets demonstrates the lower computational complexity for the video decoder and comparison with the state-of-the-art algorithms shows the efficiency of the proposed approach for 3D video transmission at different quantization (QP 26 and QP 32) and loss rate (1% and 3% packet loss) parameters along with the perceptual distortion features.
One-Step Enhancer: Deblurring and Denoising of OCT Images
Authors: Li Shunlei , Azam Muhammad Adeel , Gunalan Ajay , Mattos Leonardo S.
Published: October 2022

Optical coherence tomography (OCT) is a rapidly evolving imaging technology that combines a broadband and low-coherence light source with interferometry and signal processing to produce high-resolution images of living tissues. However, the speckle noise introduced by the low-coherence interferometry and the blur from device motions significantly degrade the quality of OCT images. Convolutional neural networks (CNNs) are a potential solution to deal with these issues and enhance OCT image quality. However, training such networks based on traditional supervised learning methods is impractical due to the lack of clean ground truth images. Consequently, this research proposes an unsupervised learning method for OCT image enhancement, termed one-step enhancer (OSE). Specifically, OSE performs denoising and deblurring based on a single step process. A generative adversarial network (GAN) is used for this. Encoders disentangle the raw images into a content domain, blur domain and noise domain to extract features. Then, the generator can generate clean images from the extracted features. To regularize the distribution range of retrieved blur characteristics, KL divergence loss is employed. Meanwhile, noise patches are enforced to promote more accurate disentanglement. These strategies considerably increase the effectiveness of GAN training for OCT image enhancement when used jointly. Both quantitative and qualitative visual findings demonstrate that the proposed method is effective for OCT image denoising and deblurring. These results are significant not only to provide an enhanced visual experience for clinicians but also to supply good quality data for OCT-guide operations. The enhanced images are needed, e.g., for the development of robust, reliable and accurate autonomous OCT-guided surgical robotic systems.
IoT Fog-Enabled Multi-Node Centralized Ecosystem for Real Time Screening and Monitoring of Health Information
Authors: Khullar Vikas , Singh Harjit Pal , Miro Yini , Anand Divya , Mohamed Heba G. , Gupta Deepali , Kumar Navdeep , Goyal Nitin
Published: October 2022

In today’s technological and stressful world, when everyone is busy in their daily routines and places blind faith in pharmaceutical advancements to protect their health, the sudden, horrifying effects of the COVID-19 pandemic have resulted in serious emotional and psychological impacts in the general population. In spite of advanced vaccination campaigns, fear and hesitation have become a part of human life since there are a number of people who do not want to take these immunity boosting vaccinations. Such people may become carriers of infectious viruses, leading to a more rapid rate of spread; therefore, this class of spreaders needs to be screened at the earliest opportunity. In this context, there is a need for advanced health monitoring systems which can assist the pharmaceutical industry to monitor and record the health status of people. To address this need and reduce the uncertainty of the situation, this study has designed and tested an Internet of Things (IoT) and Fog computing-based multi-node architecture was for real-time initial screening and recording of such subjects. The proposed system was able to record current body temperature and location coordinates along with the facial images. Further, the proposed system was able to transmit data to a cloud database using internet-connected services. An implementation and reviews-based working environment analysis was conducted to determine the efficacy of the proposed system. It was observed from the statistical analysis that the proposed IoT Fog-enabled ecosystem could be utilized efficiently.
Identification of Vehicle Loads on an Orthotropic Deck Steel Box Beam Bridge Based on Optimal Combined Strain Influence Lines
Authors: Li Cheng-Yao , Wang Chao , Yang Qing-Xiang , Qi Tian-Yu
Published: October 2022

Vehicles are critical living loads to bridge structure; thus, identifying vehicle loads is very important for structural health monitoring and safety evaluations. This paper proposed a load identification method based on an optimal combined strain influence line. Firstly, two types of strain gauges were arranged at the lower edge of a deck to monitor the strain response when vehicles cross the deck. One type of sensor was installed at the lower edge of the deck between U-ribs to detect axle information, including the number of axles, wheelbase, and vehicle speed. The other type of sensor was set on the lower edge of U-ribs to identify the axle’s weight. Secondly, structural responses under the vehicle load with known weights across the bridge was used to identify the strain influence line by using least square method. Because the local mechanical characteristic of the deck was very prominent under the wheel load, the strain influence line was short and susceptible to the transverse position of the vehicle. An index of variation coefficient is proposed as the object function, and an optimal combined strain influence line was developed using a genetic algorithm to decrease the influence of the transverse position of the load. Finally, the unknown vehicle load can be identified based on a calibrated combined strain influence line. A numerical simulation and an experimental test were carried out to validate the effectiveness and anti-noise performance of the proposed method. The identified results showed that the proposed algorithm has good accuracy and anti-noise performance.
Multi-Objective Gate Allocation Problem Based on Multi-Commodity Network Flow Model
Authors: Du Jinghan , Hu Minghua , Yin Jianan , Zhang Weining
Published: October 2022

Gate allocation has always been a fundamental but critical issue in the daily operation of airports, which is related to service quality and schedule efficiency. In order to obtain reasonable and efficient gate allocation results, in this paper, a multi-commodity network flow model is proposed to describe the gate allocation process in flight flow, based on which a multi-objective optimization model is constructed. It not only comprehensively considers the flight information of aircraft arrivals and departures, but also integrates the broader interests of passengers, airlines, and airports. To solve it, a linear weighting technique is applied. In addition, K-means cluster analysis is used to explore different weight combinations, and on this basis, the idle time of the gate is introduced as a performance evaluation index to guide the selection of the final weight. By analyzing the optimization results of actual operation data, the proposed model significantly balances the interests of multiple parties and the number of flights at each gate and has a relatively high gate-utilization rate. It can provide rich decision support and a reasonable allocation scheme for airport management to a certain extent.
Detection of Pits by Conjugate Lines: An Algorithm for Segmentation of Overlapping and Adhesion Targets in DE-XRT Sorting Images of Coal and Gangue
Authors: He Lei , Wang Shuang , Guo Yongcun
Published: October 2022

In lump coal and gangue separation based on photoelectric technology, the prerequisite of using a dual-energy X-ray to locate and identify coal and gangue is to obtain the independent target area. However, with the increase in the input of the sorting system, the actual collected images had adhesion and overlapping targets. This paper proposes a pit point detection and segmentation algorithm to solve the problem of overlapping and adhesion targets. The adhesion forms are divided into open and closed-loop adhesion (OLA and CLA). Then, an open- and closed-loop crossing algorithm (OLCA and CLCA) is proposed. We used the conjugate lines to detect the pit and judge the position and distance of the pixel point relative to the conjugate lines. Then, we set the constraint of the distance of the pixel point and the relatively straight line position to complete the pit detection. Finally, the minimum distance search method was used to obtain the dividing line corresponding to the pit to complete the image segmentation. The experiment results demonstrate that the segmentation accuracy of the overlapping target was 90.73%, and the acceptable segmentation accuracy was 94.15%.
From DevOps to MLOps: Overview and Application to Electricity Market Forecasting
Authors: Subramanya Rakshith , Sierla Seppo , Vyatkin Valeriy
Published: October 2022

In the Software Development Life Cycle (SDLC), Development and Operations (DevOps) has been proven to deliver reliable, scalable software within a shorter time. Due to the explosion of Machine Learning (ML) applications, the term Machine Learning Operations (MLOps) has gained significant interest among ML practitioners. This paper explains the DevOps and MLOps processes relevant to the implementation of MLOps. The contribution of this paper towards the MLOps framework is threefold: First, we review the state of the art in MLOps by analyzing the related work in MLOps. Second, we present an overview of the leading DevOps principles relevant to MLOps. Third, we derive an MLOps framework from the MLOps theory and apply it to a time-series forecasting application in the hourly day-ahead electricity market. The paper concludes with how MLOps could be generalized and applied to two more use cases with minor changes.
Grain Size Curve Characteristics of 2nd Member of Sangonghe Formation in Qianshao Area and Its Indicative Significance of Hydrodynamic Environment
Authors: Guo Qiaozhen , Tan Jinmiao , Li Daoqing , Lan Hao , Qiu Peng , Xu Tao , Sun Tingbin , Yin Wen
Published: October 2022

There are mainly two sedimentary types of the 2nd member of Jurassic Sangonghe Formation (J1s2) in the Qianshao area: shallow-water delta and sandy debris flow. In order to accurately understand the characteristics and further identify the difference of sedimentary microfacies, the characteristics of the grain size curve are analyzed. The results show that the cumulative probability curve of sandstone particle size in the research area mainly includes two basic types: tractive current type and sandy debris flow type. The tractive current type is mainly developed in the shallow-water delta front. Among them, the inner front subaqueous distributary channel microfacies develop two patterns: tri-segment pattern and one bouncing segment-one suspension segment-one complex transitional zone pattern. The transitional section is widely developed. There are two patterns in the subaqueous distributary channel microfacies of the outer front (bi-segment pattern and one bouncing segment-one suspension segment-one transitional zone pattern) and two patterns in the estuary bar microfacies (bi-bouncing segment-one suspension segment-one transitional zone pattern and one bouncing segment-one suspension segment-one transitional zone pattern). The main tractive current characteristics are higher bouncing component content and slope and lower suspended component content. The sand debris flow type mainly develops two patterns: one bouncing segment-one suspension segment-one transitional zone pattern and one bouncing segment-one suspension segment-one complex transitional zone pattern with lower jump component content and slope and higher suspended component content.
Special Issue “Physics and Mechanics of New Materials and Their Applications 2019”
Authors: Parinov Ivan A. , Long Banh Tien , Chang Shun-Hsyung , Wang Hung-Yu
Published: October 2022

Advanced materials and composites can be applied in a number of ways not only in modern science, techniques and technologies but in everyday life, including in the fields of aviation, medicine, waste reprocessing or the study of the underwater world [...]
Root Dilaceration and Dentigerous Cyst—A Cause–Effect Relationship?—“Case-Series Study”
Authors: Enache Aurelia Magdalena , Feștilă Dana , Boboc Florin , Nagy Evelyn Beatrix , Ghergie Mircea , Dorobanțu Mihai
Published: October 2022

Root dilaceration (DL) has a significant impact on orthodontic treatment. However, its etiology is still debatable. One of the etiologic factors of DL is the dentigerous cyst (DC); therefore, the aim of our case-series clinical study was the association between DL and DCs. This retrospective study is comprised of five clinical cases of DL associated with the presence of DCs of developmental origin. In all of these cases, other dental anomalies (impaction, transposition, supernumerary teeth, and congenitally missing teeth) were found, and accordingly, the association between DL and DCs might be of genetic origin in a dental anomaly pattern.