bluuebunny/crossref_metadata_2025 huggingface dataset:
SupMPN: Supervised Multiple Positives and Negatives Contrastive Learning Model for Semantic Textual Similarity
Authors: Dehghan Somaiyeh , Amasyali Mehmet Fatih
Published: September 2022

Semantic Textual Similarity (STS) is an important task in the area of Natural Language Processing (NLP) that measures the similarity of the underlying semantics of two texts. Although pre-trained contextual embedding models such as Bidirectional Encoder Representations from Transformers (BERT) have achieved state-of-the-art performance on several NLP tasks, BERT-derived sentence embeddings have been proven to collapse in some way, i.e., sentence embeddings generated by BERT depend on the frequency of words. Therefore, almost all BERT-derived sentence embeddings are mapped into a small area and have a high cosine similarity. Hence, sentence embeddings generated by BERT are not so robust in the STS task as they cannot capture the full semantic meaning of the sentences. In this paper, we propose SupMPN: A Supervised Multiple Positives and Negatives Contrastive Learning Model, which accepts multiple hard-positive sentences and multiple hard-negative sentences simultaneously and then tries to bring hard-positive sentences closer, while pushing hard-negative sentences away from them. In other words, SupMPN brings similar sentences closer together in the representation space by discrimination among multiple similar and dissimilar sentences. In this way, SupMPN can learn the semantic meanings of sentences by contrasting among multiple similar and dissimilar sentences and can generate sentence embeddings based on the semantic meaning instead of the frequency of the words. We evaluate our model on standard STS and transfer-learning tasks. The results reveal that SupMPN outperforms state-of-the-art SimCSE and all other previous supervised and unsupervised models.
Optimal Vehicle Lane Change Trajectory Planning in Multi-Vehicle Traffic Environments
Authors: Zhang Senlin , Deng Guohong , Yang Echuan , Ou Jian
Published: September 2022

Autonomous driving technology in urban environments is a very important avenue of research. Notably, the question of how to plan safe lane-changing trajectories is a challenge in multi-vehicle traffic environments. In our research, three kinds of polynomial lane changing mathematical models were analyzed and compared. It was found that the fifth polynomial is the most suitable for lane changing trajectories; it is defined as a generalized lane-changing trajectory cluster, whereby the minimum lane change time is determined by the vehicle lateral stability threshold. Here, a collision avoidance algorithm is proposed to eliminate unsafe trajectories. Finally, the TOPSIS algorithm is used to solve the multi-objective optimization problem, and the optimal lane-changing expected trajectory is obtained from the safe trajectory cluster. The simulation results showed improvements in lane-changing efficiency of 6.67% and no collisions in the overtaking condition. In general, the proposed method of identifying the optimal lane changing trajectory can achieve safe, efficient and stable lane changing.
Effects of Oxidized Pyrenes on the Biological Responses in the Human Bronchial Epithelial Cells
Authors: Honda Akiko , Inoue Ken-ichiro , Takai Satsuki , Kameda Takayuki , Ueda Kayo , Takano Hirohisa
Published: September 2022

Although polycyclic aromatic hydrocarbons (PAHs) are toxic, the effects of oxidized PAHs on health and biological responses remain unclear. In this study, we examined the in vitro effects of varying concentrations of pyrene, a type of PAH, and its quinone forms, namely 4,5-pyrenequinone (PyQ) and 1,8-PyQ + 1,6-PyQ, on human lung epithelial (BEAS-2B) cells. We evaluated cell viability, apoptosis, and the production of interleukin (IL)-6, IL-8, soluble intercellular adhesion molecule-1 (sICAM-1), and reactive oxygen species (ROS). Exposure to 1 μM 4,5-PyQ or 1,8-PyQ + 1,6-PyQ increased the cellular activity. At 3 µM, 4,5-PyQ increased the number of late apoptotic and/or necrotic cells compared with those in the control, whereas 1,8-PyQ + 1,6-PyQ increased the number of dead cells. Exposure to 4,5-PyQ at 10 µM decreased IL-6 production and exposure to both 4,5-PyQ and 1,8-PyQ + 1,6-PyQ at 3 or 10 µM decreased IL-8 production. sICAM-1 production was increased after 1,8-PyQ + 1,6-PyQ exposure at 10 µM. In the presence of cells, 4,5-PyQ and 1,8-PyQ + 1,6-PyQ increased ROS production significantly in a concentration-dependent manner; similar results were observed with 1,8-PyQ + 1,6-PyQ without cells. Overall, our results suggest that oxidized PAHs induce stronger respiratory toxicity/inflammatory responses than PAHs.
Simple and Efficient Non-Contact Method for Measuring the Surface of a Large Aspheric Mirror
Authors: Li Jie , Yang Jie , Chen Lin
Published: September 2022

A non-contact measurement method for measuring large aspheric surfaces with a laser tracker is proposed. Using an air-bearing probe eliminates the need to contact the optical surface and improves measurement efficiency and accuracy. Using this method, we measured the surface of an aspheric mirror 3 m in diameter and 13.6 m in the radius of curvature. The preliminary experimental result indicates that the error of surface measurement is 0.8 μm (RMS).
Research on CNN-BiLSTM Fall Detection Algorithm Based on Improved Attention Mechanism
Authors: Li Congcong , Liu Minghao , Yan Xinsheng , Teng Guifa
Published: September 2022

Falls are one of the significant causes of accidental injuries to the elderly. With the rapid growth of the elderly population, fall detection has become a critical issue in the medical and healthcare fields. In this paper, we propose a model based on an improved attention mechanism, CBAM-IAM-CNN-BiLSTM, to detect falls of the elderly accurately and in time. The model includes a convolution layer, bidirectional LSTM layer, sampling layer and dense layer, and incorporates the improved convolutional attention block module (CBAM) into the network structure so that the one-dimensional convolution layer replaces the dense layer to aggregate the information from channels, which allows the model to accurately extract different behavior characteristics. The acceleration and angular velocity data of the human body, collected by wearable sensors, are respectively input into the convolution layer and bidirectional LSTM layer of the model and then classified and identified by softmax after feature fusion. Based on comparison with models such as CNN and CNN-BiLSTM, as well as with different attention mechanisms such as squeeze-and-excitation (SE), efficient channel attention (ECA) and the convolutional block attention module (CBAM), this model improves the accuracy, sensitivity and specificity to varying degrees. The experimental results showed that the accuracy, sensitivity and specificity of the CBAM-IAM-CNN-BiLSTM model proposed in this paper were 97.37%, 97.29% and 99.56%, respectively, which proves that the model has good practicability and strong generalization ability.
The Lossless Adaptive Binomial Data Compression Method
Authors: Borysenko Oleksiy , Matsenko Svitlana , Salgals Toms , Spolitis Sandis , Bobrovs Vjaceslavs
Published: September 2022

In this paper, we propose a new method for the binomial adaptive compression of binary sequences of finite length without loss of information. The advantage of the proposed binomial adaptive compression method compared with the binomial compression method previously developed by the authors is an increase in the compression rate. This speed is accompanied in the method by the appearance of a new quality—noise immunity of compression. The novelty of the proposed method, which makes it possible to achieve these positive results, is manifested in the adaptation of the compression ratio of compressible sequences to the required time, which is carried out by dividing the initial set of binary sequences into compressible and incompressible sequences. The method is based on the theorem proved by the authors on the decomposition of a stationary Bernoulli source of information into the combinatorial and probabilistic source. The last of them is the source of the number of units. It acquires an entropy close to zero and practically does not affect the compression ratio at considerable lengths of binary sequences. Therefore, for the proposed compression method, a combinatorial source generating equiprobable sequences is paramount since it does not require a set of statistical data and is implemented by numerical coding methods. As one of these methods, we choose a technique that uses binomial numbers based on the developed binomial number system. The corresponding compression procedure consists of three steps. The first is the transformation of the compressible sequence into an equilibrium combination, the second is its transformation into a binomial number, and the third is the transformation of a binomial number into a binary number. The restoration of the compressed sequence occurs in reverse order. In terms of the degree of compression and universalization, the method is similar to statistical methods of compression. The proposed method is convenient for hardware implementation using noise-immune binomial circuits. It also enables a potential opportunity to build effective systems for protecting information from unauthorized access.
Computational Linguistics Based Emotion Detection and Classification Model on Social Networking Data
Authors: Al-Baity Heyam H. , Alshahrani Hala J. , Nour Mohamed K. , Yafoz Ayman , Alghushairy Omar , Alsini Raed , Othman Mahmoud
Published: September 2022

Computational linguistics (CL) is the application of computer science for analysing and comprehending written and spoken languages. Recently, emotion classification and sentiment analysis (SA) are the two techniques that are mostly utilized in the Natural Language Processing (NLP) field. Emotion analysis refers to the task of recognizing the attitude against a topic or target. The attitude may be polarity (negative or positive) or an emotional state such as sadness, joy, or anger. Therefore, classifying posts and opinion mining manually is a difficult task. Data subjectivity has made this issue an open problem in the domain. Therefore, this article develops a computational linguistics-based emotion detection and a classification model on social networking data (CLBEDC-SND) technique. The presented CLBEDC-SND technique investigates the recognition and classification of emotions in social networking data. To attain this, the presented CLBEDC-SND model performs different stages of data pre-processing to make it compatible for further processing. In addition, the CLBEDC-SND model undergoes vectorization and sentiment scoring process using fuzzy approach. For emotion classification, the presented CLBEDC-SND model employs extreme learning machine (ELM). Finally, the parameters of the ELM model are optimally modified by the use of the shuffled frog leaping optimization (SFLO) algorithm. The performance validation of the CLBEDC-SND model is tested using benchmark datasets. The experimental results demonstrate the better performance of the CLBEDC-SND model over other models.
Haptics and VR: Technology and Applications
Authors: Kim Sang-Youn
Published: September 2022

Although the most recently developed virtual reality systems convey photorealistic visual information and 3D audio information to users, it is not easy to create immersive and realistic sensations for users to perceive the real world [...]
Design and Simulation of a Solar Tracking System for PV
Authors: Baouche Fatima Zohra , Abderezzak Bilal , Ladmi Abdennour , Arbaoui Karim , Suciu George , Mihaltan Traian Candin , Raboaca Maria Simona , Hudișteanu Sebastian Valeriu , Țurcanu Florin Emilian
Published: September 2022

After installing a solar panel system, the orientation problem arises because of the sun’s position variation relative to a collection point throughout the day. It is, therefore, necessary to change the position of the photovoltaic panels to follow the sun and capture the maximum incident beam. This work describes our methodology for the simulation and the design of a solar tracker system using the advantages that the orientation and efficiency of the PV panel offer due to the latitude and the number of hours of sunshine in the testing area. This proposed methodology is experimentally validated through the implementation of a single-axis solar tracker at a specific location (36.261° latitude), which allowed the incorporation of a high-availability tracking mechanism, low precision, and low cost. Based on the results, the feasibility of this type of solar tracker for latitudes close to 36° was demonstrated, as this tracking system costs less than traditional commercial systems. Furthermore, this system increased the collection efficiency compared to a fixed device. Our results provide an excellent platform for engineering technology researchers and students to study the design theory of a sun-tracking solar system.
Special Issue “Cognitive Robotics”
Authors: Bandera Antonio , Fernández-Argüéllez Luis Manso , Falomir Zoe
Published: September 2022

Within the realm of new robotics, researchers have placed a great amount of effort into learning, understanding, and representing knowledge for task execution by robots [...]
Mechanism and Equivalence of Single Event Effects Induced by 14 MeV Neutrons in High-Speed QDR SRAM
Authors: Yang Shaohua , Zhang Zhangang , Lei Zhifeng , Tong Teng , Li Xiaohui , Xi Kai , Wu Fugen
Published: September 2022

Single-bit upset (SBU) and multiple-cell upset (MCU) features of high-speed QDR-SRAM are revealed under the 14 MeV neutron irradiation. By comparing with the high-altitude real atmosphere test results directly, the equivalence of 14 MeV neutrons for atmospheric neutron-induced single event effect (SEE) evaluation is investigated. It is found that, compared with the 65 nm planar device, the SBU cross-section of 14 nm FinFET SRAM decreases to 1/58 and the proportion of MCU shows little difference, which results from the narrow channel between fin and substrate caused by shallow channel isolation in 14 nm FinFET process, and the charge sharing effect between fins is weakened. The SBU and MCU cross-sections under the 14 MeV neutron irradiation are underestimated by 22.8% and 85.7%, respectively. Besides, the probability and maximum size of MCU are both smaller than those in the real atmosphere. The MCU shape tends to be vertical, resulting from the smaller vertical spacing of sensitive volumes (about 100 nm). Further Monte-Carlo simulation shows that the total yield of secondary ions produced by atmospheric neutrons is higher than that produced by 14 MeV neutrons. Major Components of the “useful” products are p, Si, α, etc., which are the main cause of SBU events. Besides, compared with 14 MeV neutrons, atmospheric neutrons generate more kinds of secondary ions in the SV within the scope from p to W, and the diverse high-Z elements, such as W, Ta, Hf, etc., are the main cause of MCU events. Moreover, the maximum LET of secondary ions can reach 31.5 MeV·cm2/mg. The equivalence of using 14 MeV neutrons for atmospheric neutron-induced SEE evaluation is closely related to the critical charge of the device under test.
An Efficient Reliability Method with Multiple Shape Parameters Based on Radial Basis Function
Authors: Du Wenyi , Ma Juan , Yue Peng , Gong Yongzhen
Published: September 2022

Structural reliability analysis has an inherent contradiction between efficiency and accuracy. The metamodel can significantly reduce the computational cost of reliability analysis by a simpler approximation. Therefore, it is crucial to build a metamodel, which achieves the minimum simulations and accurate estimation for reliability analysis. Aiming at this, an effective adaptive metamodel based on the combination of radial basis function (RBF) model and Monte Carlo simulation (MCS) is proposed. Different shape parameters are first used to generate the weighted prediction variance, and the search for new training samples is guided by the active learning function that achieves a tradeoff of (1) being close enough to limit state function (LSF) to have a high reliability sensitivity; (2) keeping enough distance between the existing samples to avoid a clustering problem; and (3) being in the sensitive region to ensure the effectiveness of the information obtained. The performance of the proposed method for a nonlinear, non-convex, and high dimensional reliability analysis is validated by three numerical cases. The results indicate the high efficiency and accuracy of the proposed method.
A Survey of Information Extraction Based on Deep Learning
Authors: Yang Yang , Wu Zhilei , Yang Yuexiang , Lian Shuangshuang , Guo Fengjie , Wang Zhiwei
Published: September 2022

As a core task and an important link in the fields of natural language understanding and information retrieval, information extraction (IE) can structure and semanticize unstructured multi-modal information. In recent years, deep learning (DL) has attracted considerable research attention to IE tasks. Deep learning-based entity relation extraction techniques have gradually surpassed traditional feature- and kernel-function-based methods in terms of the depth of feature extraction and model accuracy. In this paper, we explain the basic concepts of IE and DL, primarily expounding on the research progress and achievements of DL technologies in the field of IE. At the level of IE tasks, it is expounded from entity relationship extraction, event extraction, and multi-modal information extraction three aspects, and creates a comparative analysis of various extraction techniques. We also summarize the prospects and development trends in DL in the field of IE as well as difficulties requiring further study. It is believed that research can be carried out in the direction of multi-model and multi-task joint extraction, information extraction based on knowledge enhancement, and information fusion based on multi-modal at the method level. At the model level, further research should be carried out in the aspects of strengthening theoretical research, model lightweight, and improving model generalization ability.
Dual-Band Multiple-Element MIMO Antenna System for Next-Generation Smartphones
Authors: Kiani Saad Hassan , Marey Mohamed , SAVCI Hüseyin Şerif , Mostafa Hala , Rafique Umair , Khan Muhammad Amir
Published: September 2022

This work presents a cost-effective multiple-element multiple-input multiple-output (MIMO) antenna system for next-generation smartphones. The proposed antenna system is developed on a 0.8 mm thin FR-4 substrate with a relative permittivity of 4.4, which consists of one main board and two sideboards. The dimensions of the main board and the two side boards are 150 × 75 mm2 and 150 × 6 mm2, respectively. The radiating elements are printed on the sideboards to provide space for other radio frequency (RF) components to be embedded on the main board. The proposed antenna resonates at two distinct allotted 5G bands, i.e., 3.5 GHz and 5.4 GHz, with impedance bandwidths of 200 MHz and 700 MHz, respectively. The isolation between the antenna elements is noted to be >18 dB and >12 dB for the 3.5 GHz and 5.4 GHz frequency bands. In addition, the proposed MIMO antenna provides pattern and spatial diversity characteristics in both bands with good gain and efficiency. Furthermore, the MIMO parameters such as envelope correlation coefficient (ECC), mean effective gain (MEG), and channel capacity (CC) are calculated, and it is observed that the MIMO antenna offers good diversity performance for the bands of interest. A prototype is fabricated and measured to verify the numerical data. The simulated results were discovered to be in excellent agreement with the measured results. It is also observed that the proposed MIMO antenna system holds promising features, and can be utilized for future generations of smartphones.
Surface Crack Detection Method for Coal Rock Based on Improved YOLOv5
Authors: Chen Xinlin , Lian Qingwang , Chen Xuanlai , Shang Jin
Published: September 2022

Coal mine safety may be able to be ensured via the real-time identification of cracks in rock and coal surfaces. Traditional crack identification methods have the disadvantages of slow speed and low precision. This work suggests an improved You Only Look Once version 5 (YOLOv5) detection model. In this study, we improved YOLOv5 from the perspective of three aspects: a Ghost module was introduced into the backbone network to lighten the model; a coordinate attention mechanism was added; and ECIOU_Loss is proposed as a loss function in this paper to achieve the co-optimization of crack detection speed and accuracy and to meet the deployment requirements in the embedded terminal. The results demonstrate that the improved YOLOv5 has a 92.8% mean average precision (mAP) with an 8 MB model size, and the speed of recognition was 103 frames per second. Compared to the original method, there was a 53.4% reduction in the number of parameters, a detection speed that was 1.9 times faster, and a 1.7% improvement in the mAP. The improved YOLOv5 can effectively locate cracks in real time and offers a new technique for the early warning of coal and rock dynamic hazards.
LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals
Authors: Toro-Ossaba Alejandro , Jaramillo-Tigreros Juan , Tejada Juan C. , Peña Alejandro , López-González Alexandro , Castanho Rui Alexandre
Published: September 2022

Currently, research on gesture recognition systems has been on the rise due to the capabilities these systems provide to the field of human–machine interaction, however, gesture recognition in prosthesis and orthesis has been carried out through the use of an extensive amount of channels and electrodes to acquire the EMG (Electromyography) signals, increasing the cost and complexity of these systems. The scientific literature shows different approaches related to gesture recognition based on the analysis of EMG signals using deep learning models, highlighting the recurrent neural networks with deep learning structures. This paper presents the implementation of a Recurrent Neural Network (RNN) model using Long-short Term Memory (LSTM) units and dense layers to develop a gesture classifier for hand prosthesis control, aiming to decrease the number of EMG channels and the overall model complexity, in order to increase its scalability for embedded systems. The proposed model requires the use of only four EMG channels to recognize five hand gestures, greatly reducing the number of electrodes compared to other approaches found in the literature. The proposed model was trained using a dataset for each gesture EMG signals, which were recorded for 20 s using a custom EMG armband. The model reached an accuracy of to 99% for the training and validation stages, and an accuracy of 87 ± 7% during real-time testing. The results obtained by the proposed model establish a general methodology for the reduction of complexity in the recognition of gestures intended for human.machine interaction for different computational devices.
A Self-Adaptive Vibration Reduction Method Based on Deep Deterministic Policy Gradient (DDPG) Reinforcement Learning Algorithm
Authors: Jin Xin , Ma Hongbao , Tang Jian , Kang Yihua
Published: September 2022

Although many adaptive techniques for active vibration reduction have been designed to achieve optimal performance in practical applications, few are related to reinforcement learning (RL). To explore the best performance of the active vibration reduction system (AVRS) without prior knowledge, a self-adaptive parameter regulation method based on the DDPG algorithm was examined in this study. The DDPG algorithm is unsuitable for a random environment and prone to reward-hacking. To solve this problem, a reward function optimization method based on the integral area of the decibel (dB) value between transfer functions was investigated. Simulation and graphical experimental results show that the optimized DDPG algorithm can automatically track and maintain optimal control performance of the AVRS.
A Multiple-Choice Maze-like Spatial Navigation Task for Humans Implemented in a Real-Space, Multipurpose Circular Arena
Authors: Muela Pablo , Cintado Elisa , Tezanos Patricia , Fernández-García Benjamín , Tomás-Zapico Cristina , Iglesias-Gutiérrez Eduardo , Díaz Martínez Angel Enrique , Butler Ray G. , Cuadrado-Peñafiel Victor , De la Vega Ricardo , Soto-León Vanesa , Oliviero Antonio , López-Mascaraque Laura , Trejo José Luis
Published: September 2022

Spatial navigation is a key aspect of human behavior and it is still not completely understood. A number of experimental approaches exist, although most of the published data in the last decades have relied on virtual maze on-screen simulation or not-completely freely moving 3D devices. Some interesting recent developments, such as circular mazes, have contributed to analyze critical aspects of freely moving human spatial navigation in real space, although dedicated protocols only allow for simple approaches. Here, we have developed both specifically designed and home-assembled hardware equipment, and a customized protocol for spatial navigation evaluation in freely moving humans in a real space circular arena. The spatial navigation protocol poses an imitation of a real-space multiple-choice path maze with cul-de-sac and instances of non-linear movement. We have compared the results of this system to those of a number of validated, both virtual and real, spatial navigation tests in a group of participants. The system composed by hardware, the test protocol, and dedicated measure analysis designed in our laboratory allows us to evaluate human spatial navigation in a complex maze with a small and portable structure, yielding a highly flexible, adaptable, and versatile access to information about the subjects’ spatial navigation abilities.
Constant Force Control of Centrifugal Pump Housing Robot Grinding Based on Pneumatic Servo System
Authors: Su Xueman , Xie Yueyue , Sun Lili , Jiang Benchi
Published: September 2022

In order to solve the problem of constant force control in the robot grinding process of a centrifugal pump housing a circular inner surface, this study used the force–position hybrid control mode based on a pneumatic servo system to realize the constant control of grinding force. In this process, the manipulator realizes the position and pose control of the end grinding device, and the end grinding device realizes the constant force control in the grinding process. The mathematical model of the pneumatic system is established and linearized by using the gas balance state equation, the adiabatic equation of the isentropic process, and the Sanville flow equation. The balance equation of the cylinder piston was established by using Newton’s second law, the transfer function of the contact force between the grinding device and workpiece was obtained, and the stability of the pneumatic control system was determined by the Hurwitz criterion. The PID algorithm was used to improve the displacement response speed of the system and eliminate the impact and oscillation in the force response. The feasibility, stability, and robustness of the system were verified by simulation experiments. This method has the advantages of simple control, a small amount of calculation, and a fast response, as well as providing a feasible scheme for the popularization and application of robot grinding technology.
AHA-AO: Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification
Authors: Elaziz Mohamed Abd , Dahou Abdelghani , El-Sappagh Shaker , Mabrouk Alhassan , Gaber Mohamed Medhat
Published: September 2022

This paper presents a system for medical image diagnosis that uses transfer learning (TL) and feature selection techniques. The main aim of TL on pre-trained models such as MobileNetV3 is to extract features from raw images. Here, a novel feature selection optimization algorithm called the Artificial Hummingbird Algorithm based on Aquila Optimization (AHA-AO) is proposed. The AHA-AO is used to select only the most relevant features and ensure the improvement of the overall model classification. Our methodology was evaluated using four datasets, namely, ISIC-2016, PH2, Chest-XRay, and Blood-Cell. We compared the proposed feature selection algorithm with five of the most popular feature selection optimization algorithms. We obtained an accuracy of 87.30% for the ISIC-2016 dataset, 97.50% for the PH2 dataset, 86.90% for the Chest-XRay dataset, and 88.60% for the Blood-cell dataset. The AHA-AO outperformed the other optimization techniques. Moreover, the developed AHA-AO was faster than the other feature selection models during the process of determining the relevant features. The proposed feature selection algorithm successfully improved the performance and the speed of the overall deep learning models.
FAECCD-CNet: Fast Automotive Engine Components Crack Detection and Classification Using ConvNet on Images
Authors: Berwo Michael Abebe , Fang Yong , Mahmood Jabar , Yang Nan , Liu Zhijie , Li Yimeng
Published: September 2022

Crack inspections of automotive engine components are usually conducted manually; this is often tedious, with a high degree of subjectivity and cost. Therefore, establishing a robust and efficient method will improve the accuracy and minimize the subjectivity of the inspection. This paper presents a robust approach towards crack classification, using transfer learning and fine-tuning to train a pre-trained ConvNet model. Two deep convolutional neural network (DCNN) approaches to training a crack classifier—namely, via (1) a Light ConvNet architecture from scratch, and (2) fined-tuned and transfer learning top layers of the ConvNet architectures of AlexNet, InceptionV3, and MobileNet—are investigated. Data augmentation was utilized to minimize over-fitting caused by an imbalanced and inadequate training sample. Data augmentation improved the accuracy index by 4%, 5%, 7%, and 4%, respectively, for the proposed four approaches. The transfer learning and fine-tuning approach achieved better recall and precision scores. The transfer learning approach using the fine-tuned features of MobileNet attained better classification accuracy and is thus proposed for the training of crack classifiers. Moreover, we employed an up-to-date YOLOv5s object detector with transfer learning to detect the crack region. We obtained a mean average precision (mAP) of 91.20% on the validation set, indicating that the model effectively distinguished diverse engine part cracks.
Cytotoxicity and Antimicrobial Activity of BioAktTM and Phytic Acid: A Laboratory-Based Study
Authors: Bertoletti Paolo , Salvadori Matteo , Tonini Riccardo , Forgione Diletta , Francinelli Jacopo , Garo Maria Luisa , Salgarello Stefano
Published: September 2022

(1) Background: To improve endodontic treatments, it is necessary to find good irrigant solutions that balance potential toxic effects with optimal antimicrobial capacity. The aim of this laboratory study was to assess the cytotoxic and antimicrobial capacity of phytic acid (IP6) and BioAktTM in a laboratory setting to determine their suitability prior to endodontic evaluation. (2) Methods: The antimicrobial and cytotoxic effects of IP6 and BioAktTM were compared with those of 1.5% NaOCl. Cytotoxicity was evaluated via crystal violet assay and CellTiter-Glo® 2.0 assay, while antimicrobial capacity was tested via disk diffusion and the effect of a 1-h treatment on Enterococcus faecalis. (3) Results: A crystal violet test and CellTiter-Glo® 2.0 assay showed statistically significant differences in cell viability and cell activity after 4 and 24 h for all tested solutions (p < 0.05). The antimicrobial activity of BioAktTM was similar to that of 1.5% NaOCl, while phytic acid showed the lowest antimicrobial activity compared to BioAktTM (p < 0.05) and 1.5% NaOCl (p < 0.05). (4) Conclusion: BioAktTM showed an optimal balance between antimicrobial activity and cytotoxicity.
The Accumulation Characteristics of Different Heavy Metals in Sea Rice
Authors: Li Jiachun , Yang Xu , Zheng Yuze , Tang Lele , Lai Zhenai , Liu Na , Li Huashou
Published: September 2022

Here heavy metal (Cd, Zn, and Cu) accumulation features of sea rice (a salt-tolerant rice cultivar) were explored to provide a reference for preventing and controlling of heavy metals, screen new plant remediation resources, and offer the basis for safe sea rice production. The sea rice variety Hai Hong 12 (HH12) was used as the research object in the soil culture experiments conducted to investigate the effects of the addition of Cd (0, 1, 2, 4, 8 mg/kg), Zn (0, 100, 200, 300, 400 mg/kg), and Cu (0, 100, 200, 300, 400 mg/kg) on the growth and accumulation of heavy metals in different HH12 parts. At different Cd and Zn concentrations, the root biomass of HH12 decreased significantly and the shoot biomass showed no marked difference; however, Cu stress inhibited the rice biomass. The Cd content in different parts of sea rice increased with an increase in Cd levels. At 8 mg/kg, shoot and spike had the highest Cd content, which was 89.02 and 45.37 mg/kg, respectively. At 1 mg/kg Cd concentration, the Cd transport coefficient of sea rice was the highest (1.36). The Zn content was the highest in sea rice at 400 mg/kg Zn. Zn concentrations in the shoot and spike were 440.95 and 175.51 mg/kg. However, the Zn transport coefficients of all parts were <1 at different Zn concentrations. Sea rice growth was severely hampered by high Cu stress (400 mg/kg). After 200 mg/kg Cu treatment, the highest Cu content was 82.85 mg/kg in shoot and 46.97 mg/kg in spike. The Cu transport coefficients of all parts were also <1 under Cu stress and decreased with an increase in the Cu concentration. In summary, HH12 exhibited a high risk of Cd accumulation, and Cd was more likely to be transported to the grains. Zn accumulation in sea rice had no obvious toxicity to its shoot growth, but its shoot had a slight risk of Zn accumulation. Cu was mostly built up in the HH12 roots, but its ability to move around was low. However, high Cu concentrations slowed the growth of sea rice.
Preliminary Approach for Open Lateral Window Technique for Successful Maxillary Sinus Augmentation in the Unrepairable Wide Perforation Area of Schneiderian Membrane
Authors: Park Won-Bae , Crasto Gazelle Jean , Kang Philip
Published: September 2022

During maxillary sinus augmentation (MSA), bone graft particles displaced through the perforated site can block the ostium and become a risk factor for maxillary sinusitis. The purpose of this case report is to introduce a novel approach for successful lateral MSA performed in the unrepairable perforation area of Schneiderian membrane. In a total of three patients, including two who were unintentionally treated with a split-mouth design, the Schneiderian membrane was irreparably perforated during the sinus floor elevation. After bone grafting was performed on only the sinus floor, the open lateral window technique was performed in which the upper part of the lateral window was opened. After the procedure, unexpectedly, the patient showed transient nasal bleeding, but no unusual clinical events. Most of the bone graft substitutes were not displaced and the exposed portion was covered with a soft tissue. In the sinus graft, clotting of blood, supplied from the perforated Schneiderian membrane and the buccal flap through the open lateral window, appeared to prevent graft displacement. Within the limitations of present case reports, these patients showed that MSA via the open lateral window technique was possible even with a widely perforated Schneiderian membrane that could not be repaired.
Lightweight Transformer Network for Ship HRRP Target Recognition
Authors: Yue Zhibin , Lu Jianbin , Wan Lu
Published: September 2022

The traditional High-Resolution Range Profile (HRRP) target recognition method has difficulty automatically extracting target deep features, and has low recognition accuracy under low training samples. To solve these problems, a ship recognition method is proposed based on the lightweight Transformer model. The model enhances the representation of key features by embedding Recurrent Neural Networks (RNN) into Transformer’s encoder. The Group Linear Transformations (GLTs) are introduced into Transformer to reduce the number of parameters in the model, and stable features are extracted through linear intergroup dimensional transformations. The adaptive gradient clipping algorithm is combined with the Stochastic Gradient Descent (SGD) optimizer to allow the gradient to change dynamically with the training process and to improve the training speed and generalization ability of the model. Experimental results on the simulated dataset show that multi-layer model stacking can effectively extract deep features of targets and raise recognition accuracy. At the same time, the lightweight Transformer model can maintain good recognition performance with low parameters and low training samples.
A Stacking Heterogeneous Ensemble Learning Method for the Prediction of Building Construction Project Costs
Authors: Park Uyeol , Kang Yunho , Lee Haneul , Yun Seokheon
Published: September 2022

The accurate cost estimation of a construction project in the early stage plays a very important role in successfully completing the project. In the initial stage of construction, when the information necessary to predict construction cost is insufficient, a machine learning model using past data can be an alternative. We suggest a two-level stacking heterogeneous ensemble algorithm combining RF, SVM and CatBoosting. In the step of training the base learner, the optimal hyperparameter values of the base learners were determined using Bayesian optimization with cross-validation. Cost information data disclosed by the Public Procurement Service in South Korea are used to evaluate ML algorithms and the proposed stacking-based ensemble model. According to the analysis results, the two-level stacking ensemble model showed better performance than the individual ensemble models.
Comparison of Different Approaches of Machine Learning Methods with Conventional Approaches on Container Throughput Forecasting
Authors: Xu Shuojiang , Zou Shidong , Huang Junpeng , Yang Weixiang , Zeng Fangli
Published: September 2022

Container transportation is an important mode of international trade logistics in the world today, and its changes will seriously affect the development of the international market. For example, the COVID-19 pandemic has added a huge drag to global container logistics. Therefore, the accurate forecasting of container throughput can make a significant contribution to stakeholders who want to develop more accurate operational strategies and reduce costs. However, the current research on port container throughput forecasting mainly focuses on proposing more innovative forecasting methods on a single time series, but lacks the comparison of the performance of different basic models in the same time series and different time series. This study uses nine methods to forecast the historical throughput of the world’s top 20 container ports and compares the results within and between methods. The main findings of this study are as follows. First, GRU is a method that can produce more accurate results (0.54–2.27 MAPE and 7.62–112.48 RMSE) with higher probability (85% for MAPE and 75% for RMSE) when constructing container throughput forecasting models. Secondly, NM can be used for rapid and simple container throughput estimation when computing equipment and services are not available. Thirdly, the average accuracy of machine learning forecasting methods is higher than that of traditional methods, but the accuracy of individual machine learning forecasting methods may not be higher than that of the best conventional traditional methods.
Performance Optimization Design of Diagonal Flow Fan Based on Ensemble of Surrogates Model
Authors: Zhou Shuiqing , Lu Laifa , Xu Biao , He Jiacheng , Xia Ding
Published: September 2022

Due to the advantages of a high total pressure coefficient, large flow coefficient, and high efficiency, the diagonal flow fan is widely used in people’s livelihood and industrial fields. However, the design of the diagonal flow fan is mostly empirical, multi-solution, and comprehensive. The traditional optimization design process often consumes huge computing resources. In this paper, the diagonal flow fan blade is parameterized, the design variables are determined, and the accuracy of the parameterization method is verified. The maximum fitting error is controlled at approximately 0.1%. Based on the parametric design of blades, this paper organically integrates the traditional Kriging model and RBF model, and introduces the Ensemble of surrogates model (ES) to verify that the ES model has higher prediction accuracy in the prediction of fan flow and total pressure efficiency than the traditional prediction model. Subsequently, the Pareto optimal solution set of the approximate model within the global design scope is searched by NSGA-II. The numerical simulation and experimental verification show that the actual flow of the fan increases by 10% and the efficiency of the full pressure increases by 3.2% under the design condition of the optimized blade. The optimized model can significantly improve its air performance.
Control Strategy of Speed Segmented Variable Constant Power Powertrain of Electric Construction Machinery
Authors: Li Zhongshen , Lin Yuanzheng , Chen Qihuai , Wu Kai , Lin Tianliang , Ren Haoling , Gong Wen
Published: September 2022

Energy conservation and emission reduction have become a global development consensus. Traditional construction machinery driven by an engine has high energy consumption and poor emission. Electric construction machinery is considered to be one of the main trends in the future due to its zero emissions by canceling the engine and using the motor-driven hydraulic system. However, most of the existing electric construction machinery works through the motor to simulate the engine without considering the regulation characteristics of the motor. Therefore, although the existing electric construction machinery improves the emission and the energy efficiency of the power system to a certain extent, the control of the motor and hydraulic systems can still be further optimized. The energy efficiency of the whole machine can be maximized. Based on the LUDV system of construction machinery, a control strategy based on motor speed classification and variable constant power can automatically identify the actual working conditions of the electric excavator and adjust the starting pressure of the constant power valve, to change the constant power range of hydraulic pump and achieve the goal of adapting to the working conditions of power system, is proposed. Simulation and experiments are carried out to verify the feasibility of the proposed control strategy. The results show that the speed classification and variable constant power control system can effectively realize the hierarchical regulation of motor speed and provide relatively stable speed input for the hydraulic system. Moreover, the current working condition can be identified through the pump outlet pressure. The adaption of the working conditions can be realized through the proportional reducing valve by adjusting the starting pressure of the variable constant power valve.
Effects of Corner Modification on the Wind-Induced Responses of High-Rise Buildings
Authors: Ke Yanyu , Shen Guohui , Yu Hangcong , Xie Jiming
Published: September 2022

Aerodynamic optimization of building geometry has received significant attention in the design community. In this paper, a process with the high-frequency force balance (HFFB) technique to determine the most effective mitigation measure and the synchronized pressure integration (SPI) technique to verify the effect is developed for the aerodynamic optimization of high-rise buildings. Then, the process is applied to a 318 m-tall high-rise building. Tests show that the wind force on the building will not be symmetrical about the wind azimuth due to the interfering effect. The standard deviation of the base bending moment in the cross-wind direction is much larger than that in the along-wind direction. It indicates that the cross-wind loads will be dominated, providing a remarkable building height. The aerodynamic treatment of corner modifications has a considerable benefit in reducing the cross-wind loads and responses. Among the four corner modifications, the model with a 10% roundness radius to width ratio has the best mitigation effect in the along wind and cross-wind direction. Furthermore, the mean and extreme base overturning moments obtained by the SPI and the HFFB tests almost coincided with wind azimuth with acceptable discrepancy.
Measurement-Driven Synthesis of Female Digital Mannequin Using Convex Sub-Volumes
Authors: Velez-Sanin Samuel , Gutierrez Juan , Correa Jorge , Builes-Roldan Carolina , Ruiz-Salguero Oscar
Published: September 2022

In the context of computer-aided apparel-fitting simulation, the problem of generating (a) simulation-inexpensive and (b) tailor-measurement-driven digital mannequins is central. Three-dimensional scanning of human bodies produces high-fidelity datasets. However, this technique does not satisfy conditions (a) and (b) above. In addition, it requires extensive data cleaning and processing. Existing approaches to this problem broadly fall into these mainstreams: (i) biased scaling, interpolation, or morphing of template models; or (ii) bottom-up construction of anatomy (bone medial axis, kinematic joints, muscles, skin, and other layers). Both alternatives imply extensive scanning, application of heuristics, tuning, and storage, among other tasks. Both alternatives produce non-convex datasets that have to be processed further for cloth–body interaction simulation, as physics engines require some type of data convexity for realistic simulations. This manuscript presents a modeling methodology that partially overcomes these limitations by (1) coarsely approximating a template female body with sets of convex volumes (ellipsoids and cushions), (2) building a set of Reference Mannequins for a particular set of extreme and average tailor measurements, and (3) creating sets of functions that synthesize new individuals of digital mannequins as reunions of convex volumes that satisfy specified tailor measurements. These mannequins present a reasonable and realistic demeanor. At the same time, they are shown to be economical at the stage of simulation of garment fitting. Future work is encouraged to define kinematic chains for straightforward pose definition, modeling male bodies, and exploring the behavior of the synthesis functions with more parameters.
Energy Bidding Quadratic Model and the Use of the B-Loss Matrix for Determining Consumer Energy Price
Authors: Raharjo Jangkung , Zein Hermagasantos
Published: September 2022

The liberalization trend has led to electric restructuring in market industries. At the start of the 1990s, it was recommended to shift the electricity business from a monopoly to a competitive system. The electric power problem becomes more complex from competition because competitors must be ready to win or lose. The method that has succeeded in determining energy prices in competition is the locational marginal price method implemented by the New York Service Operator. In general, the characteristic of the supplier offers are in step function forms, so optimizing prices and allocating transmission losses are a problem. This paper proposes a method for determining electrical energy prices on the consumer side in each location. The method uses a quadratic approach to perform direct method optimization. The transmission losses are calculated through the B-loss matrix approach, and then allocations of the transmission losses are separated with the proportional method. Simulation results for three locations with six suppliers, as well as on a larger scale (118 buses, 54 generators) were obtained.