Hasil untuk "Transportation engineering"

Balvender Singh, Pushpendra Singh, Ghanshyam G. Tejani et al.

Hongbo Yin, Daxin Tian, Jianshan Zhou

Tomas Mickevičius, Agnieszka Dudziak, Jonas Matijošius et al.

In the pursuit of sustainable and circular energy sources, this study examines the potential of tire pyrolysis oil (TPO) as a diesel fuel substitute when combined with hydrotreated vegetable oil (HVO), a second-generation biofuel. At varying TPO-HVO blend percentages, this investigation evaluates engine performance and emissions in relation to critical fuel parameters, including density, viscosity, and lubricity. The high-frequency reciprocating rig (HFRR) method was employed to examine tribological aspects, and a single-cylinder diesel engine was tested under various load conditions. The findings indicated that blends containing up to 30% TPO maintained sufficient lubrication and engine performance to comply with diesel standards, concurrently reducing carbon monoxide and smoke emissions. The increase in TPO proportion resulted in a decrease in cetane number, an increase in NOx emissions, and a rise in viscosity, particularly under full engine load conditions. The utilization of TPO is crucial for converting tire waste into fuel, as it mitigates the accumulation of tire waste and reduces dependence on fossil fuels, despite existing challenges. This study provides critical insights into the efficacy of blending methods and underscores the necessity of additional fuel refining processes, such as cetane enhancement and desulfurization, to facilitate their integration into transportation energy systems.

Josue Ortega, Martin Ortega, Karzan Ismael et al.

Nazmus Sakib, Tonmoy Paul, Nafis Anwari et al.

Shekoofeh Vafaei, Masoud Yaghini

Vyacheslav Voronin, Fedor Nepsha, Pavel Ilyushin

In this paper, a method for determining the parameters of the Volt/Var characteristics of inverters of electric vehicle charging stations to regulate voltage in distribution networks is proposed, which differs from the existing ones by taking into account the possibility of the joint control of active and reactive power and the impedance of the power distribution line. The method proposed in this paper allows researchers to determine the slope and width of the dead band of the Volt/Var characteristics according to the criterion of limiting the maximum voltage deviations to an acceptable value or maximizing the reactive power of the inverter upon reaching a specified voltage. To test this method, a quasi-dynamic modeling of the distribution network with electric vehicle charging stations regulating voltage using the Volt/Var characteristics was performed. Based on the modeling results, it is shown that fast electric vehicle charging stations can be used to regulate voltage in the distribution network with relatively minor constraints on the charging active power.

Fu Zhu, Yu Xin, Wei Tian et al.

The service performance of chromium-free zinc–aluminum coatings exhibits characteristics from multiple perspectives. Fully considering the physical properties, corrosion resistance, and economic viability of the coatings, this study incorporates the concepts of “domain” and “degree” from extenics theory into the analytic hierarchy process to optimize the formulation of chromium-free zinc–aluminum coatings. The findings reveal that the extension analytic hierarchy process takes into account the diversity of evaluation indicators, enhancing the objectivity and accuracy of the comprehensive evaluation results. Nine formulations were developed using a four-factor, three-level orthogonal experiment to evaluate the effects of metal powder, PEG-400, KH-560, and sodium molybdate on the service performance of chromium-free zinc–aluminum coatings. Utilizing an extensible hierarchical sorting weight system alongside a performance index grading and scoring method, 3# emerged with the highest score, indicating the best overall performance. The research outcomes offer innovative insights and technical support for optimizing the formulations of chromium-free zinc–aluminum coatings and other coatings.

Jingxun Cai, Zne-Jung Lee, Zhihxian Lin et al.

Ovarian cancer is one of the most aggressive gynecological cancers due to its high invasion and chemoresistance. It not only has a high incidence rate but also tops the list of mortality rates. Its subtle early symptoms make subsequent diagnosis difficult, significantly delaying timely treatment for patients. Once ovarian cancer reaches an advanced stage, the complexity and difficulty of treatment increase substantially, affecting patient survival rates. Therefore, it is crucial for both medical professionals and patients to remain highly vigilant about the early signs of ovarian cancer to ensure timely intervention. In recent years, ovarian cancer prediction research has advanced, allowing for the analysis of the likelihood and type of cancer based on patients’ genetic data. With the rapid development of machine learning, numerous efficient classification prediction models have emerged. These new technologies offer significant opportunities and potential for developing ovarian cancer diagnostic prediction methods. However, traditional approaches often struggle to achieve satisfactory classification accuracy in high-dimensional genetic datasets with small sample sizes. This research offers a prediction model utilizing genomic data to enhance the early diagnosis rate of ovarian cancer, incorporating feature selection, data augmentation through adversarial conditional generative adversarial networks (AC-GAN), and an extreme gradient boosting (XGBoost) classifier. First, we can simplify the original genetic dataset through feature selection methods, removing irrelevant variables and noise, thereby improving the model’s predictive accuracy. Following dimensionality reduction, AC-GAN enriches the data, producing more realistic genetic samples to enhance the model’s generalization capacity. Finally, the XGBoost classifier is applied to classify the augmented data, achieving efficient predictions for ovarian cancer. These research findings strongly demonstrate that the diagnostic method proposed in this paper has a significant advantage in the predictive diagnosis of ovarian cancer, with an accuracy of 99.01% that surpasses the current technologies in use. Additionally, the algorithm identifies twelve genes highly relevant to ovarian cancer, providing valuable insights for physicians during diagnosis.

Natasha Bahrani, Juliette Blanc, Pierre Hornych et al.

Peyman Haghgooei, Ehsan Jamshidpour, Adrien Corne et al.

This paper presents a new online method based on low frequency signal injection to estimate the stator resistance of a Wound Rotor Synchronous Machine (WRSM). The proposed estimator provides a parameter-free method for estimating the stator resistance, in which there is no need to know the values of the parameters of the machine model, such as the stator and rotor inductances or the rotor flux linkage. In this method, a low frequency sinusoidal current is injected in the <i>d</i> axis of the stator current to produce a sinusoidal flux in the stator. In this paper, it is shown that the phase difference between the generated sinusoidal flux and the injected sinusoidal current is related to the stator resistance mismatch. Using this phase difference, the stator resistance is estimated. To validate the proposed model-free estimator, simulations were performed with Matlab Simulink and the results were compared with the extended Kalman filter observer. Finally, experimental tests, under different conditions, were performed to estimate the stator resistance of a WRSM.

Ekkachai Yooprasertchai, Alireza Bahrami, Panumas Saingam et al.

Each year, an enormous amount of construction waste is produced worldwide. The reuse of construction waste in construction works is a sustainable solution. The present research work utilized recycled brick aggregates in the production of concrete. The resulting concrete exhibited substandard splitting tensile, flexural, and compressive properties. Steel fibers were used to improve these substandard properties of recycled brick aggregate concrete. The volume fractions of 1%, 2%, and 3% for steel fibers were mixed in concrete, whereas recycled brick aggregates were obtained from solid fired-clay bricks, hollow fired-clay bricks, and cement–clay interlocking bricks. The compressive strength was enhanced by up to 35.53% and 66.67% for natural and recycled brick aggregate concrete, respectively. Strengthened flexural specimens demonstrated up to 8765.69% increase in the energy dissipation. Specimens strengthened with steel fibers showed substantially improved splitting tensile, flexural, and compressive responses. Separate equations were proposed to predict the peak compressive strength, strain at peak compressive strength, elastic modulus, and post-peak modulus of recycled brick aggregate concrete. The proposed regression equations were utilized in combination with an existing compressive stress–strain model. A close agreement was observed between experimental and predicted compressive stress–strain curves of recycled brick aggregate concrete.

Michael Alfred Stephenson Biharta, Sigit Puji Santosa, Djarot Widagdo et al.

This research study involves designing and optimizing a sandwich structure based on an auxetic structure to protect the pouch battery system for electric vehicles undergoing ground impact load. The core of the sandwich structure is filled with the auxetic structure that has gone through optimization to maximize the specific energy absorbed. Its performance is analyzed with the non-linear finite element method. Five geometrical variables of the auxetic structures are analyzed using the analysis of variance and optimized using Taguchi’s method. The optimum control variables are double-U hierarchal (DUH), the cross-section’s thickness = 2 mm, the length of the cell = 10 mm, the width of the cell = 17 mm, and the bending height = 3 mm. The optimized geometries are then arranged into three different sandwich structure configurations. The core is filled with optimized DUH cells that have been enlarged to 200% in length, arranged in 11 × 11 × 1 cells, resulting in a total dimension and mass of 189 × 189 × 12 mm and 0.75 Kg. The optimized sandwich structure shows that the pouch battery cells can be protected very well from ground impact load with a maximum deformation of 1.92 mm, below the deformation threshold for battery failure.

Yongling Li, Stan Geertman, Yanliu Lin et al.

Studies have found that spatial mismatch is a universal phenomenon, although both their substantive and methodological focus can differ substantially. In China, there is a growing body of literature on spatial mismatch, but few studies have measured the degree of spatial mismatch between local and migrant workers in different occupations. To fill this gap, this research investigates the spatial mismatch for different socioeconomic groups in Xiamen according to their “hukou” status and occupation. As one of the country’s first four special economic zones, Xiamen achieved housing marketization earlier than most other Chinese cities, attracting a large amount of capital and migrants, and shaping different spatial patterns of local workers and migrant workers. The findings show that blue-collar, pink-collar, and white-collar workers, who are further categorized as either locals or migrants, experience varying degrees of job accessibility and spatial mismatch. In addition, even though migrant workers experience less spatial mismatch, they still have disadvantages in terms of commuting time due to their travel mode. The results presented in this paper are helpful for understanding the spatial mismatch for various social groups and facilitating sustainable mobility and social equity.

Raja Manish, Seyyed Meghdad Hasheminasab, Jidong Liu et al.

Stockpile quantity monitoring is vital for agencies and businesses to maintain inventory of bulk material such as salt, sand, aggregate, lime, and many other materials commonly used in agriculture, highways, and industrial applications. Traditional approaches for volumetric assessment of bulk material stockpiles, e.g., truckload counting, are inaccurate and prone to cumulative errors over long time. Modern aerial and terrestrial remote sensing platforms equipped with camera and/or light detection and ranging (LiDAR) units have been increasingly popular for conducting high-fidelity geometric measurements. Current use of these sensing technologies for stockpile volume estimation is impacted by environmental conditions such as lack of global navigation satellite system (GNSS) signals, poor lighting, and/or featureless surfaces. This study addresses these limitations through a new mapping platform denoted as Stockpile Monitoring and Reporting Technology (SMART), which is designed and integrated as a time-efficient, cost-effective stockpile monitoring solution. The novel mapping framework is realized through camera and LiDAR data-fusion that facilitates stockpile volume estimation in challenging environmental conditions. LiDAR point clouds are derived through a sequence of data collections from different scans. In order to handle the sparse nature of the collected data at a given scan, an automated image-aided LiDAR coarse registration technique is developed followed by a new segmentation approach to derive features, which are used for fine registration. The resulting 3D point cloud is subsequently used for accurate volume estimation. Field surveys were conducted on stockpiles of varying size and shape complexity. Independent assessment of stockpile volume using terrestrial laser scanners (TLS) shows that the developed framework had close to 1% relative error.

Jianhe Li, Weizhe Sun, Guoshao Su et al.

The geomechanical parameters in underground engineering are usually difficult to determine, which can pose great obstacles in underground engineering. A novel displacement back-analysis method is proposed to determine the geomechanical parameters in underground engineering. In this method, the problem of geomechanical parameter determination is converted into an optimization problem, regarding the geomechanical parameters as the optimization parameters, and the error between the calculated results and the field measurement information as the optimization objective function. The grasshopper optimization algorithm (GOA), which offers excellent global optimization performance, and the Gaussian process regression (GPR) machine learning, offering powerful fitting ability, are combined to address the time-consuming numerical calculations. Furthermore, the proposed method is combined with the 3D numerical calculation software FLAC^3D to form the GOA-GPR-FLAC^3D method, which can be used in the displacement back-analysis of geomechanical parameters in underground engineering. The results of a case study show that the proposed method can greatly improve computational efficiency while ensuring high precision compared with the GOA. When applied to the Tai’an Pumped Storage Power Station, this method can obtain more accurate results compared with the GOA under the same evaluation times and is more suitable for the back-analysis of rock parameters in underground engineering.

Xin Huang, Yuming Wang, Yazhou Chen

Aiming at the problem that satellite navigation signals are easily interfered by the radio frequency (RF) pulse signal, the electromagnetic interference effect of RF pulse on the navigation receiver is studied in this paper. A mathematical model of the pulse interference signal is established, and we choose the bit error rate (BER) as an indicator of the quality of the BDS signal. It is found that the BER is proportional to the duty cycle of the pulse signal and inversely proportional to the equivalent carrier-to-noise ratio (C/N0) through simulation. Then, an experiment of electromagnetic injection on the BDS receiver has been carried out, which studied the influence of the pulse interference parameters, such as repetition frequency and duty cycle on the C/N0 of the BDS signal and the electromagnetic sensitivity threshold of the receiver. The comparative experiment between the pulse interference and the single-frequency continuous wave (CW) interference was also carried out, and we found that the effect of the pulse interference is better than that of single-frequency CW interference. The former is the correlation interference, and the latter is the blocking interference. Combined with the experiment phenomenon, the interference mechanism was further analyzed according to the relationship between the pulse period and the ranging code period of the navigation signal.

Daniel Atuah Obeng, Victor Owusu, Yaw A. Tuffour

Zhou Zhu, Haifeng Zhao, Fang Hui et al.

In this paper, we address the problem of online updating of visual object tracker for car sharing services. The key idea is to adjust the updating rate adaptively according to the tracking performance of the current frame. Instead of setting a fixed weight for all the frames in the updating of the object model, we assign the current frame a larger weight if its corresponding tracking result is relatively accurate and unbroken and a smaller weight on the contrary. To implement it, the current estimated bounding box’s intersection over union (IOU) is calculated by an IOU predictor which is trained offline on a large number of image pairs and used as a guidance to adjust the updating weights online. Finally, we imbed the proposed model update strategy in a lightweight baseline tracker. Experiment results on both traffic and nontraffic datasets verify that though the error of predicted IOU is inevitable, the proposed method can still improve the accuracy of object tracking compared with the baseline object tracker.

I. V. Zhukovyts'kyy, V. M. Pakhomova, D. O. Ostapets et al.

Purpose. The article is aimed at the development of a methodology for detecting attacks on a computer network. To achieve this goal the following tasks were solved: to develop a methodology for detecting attacks on a computer network based on an ensemble of neural networks using normalized data from the open KDD Cup 99 database; when performing machine training to identify the optimal parameters of the neural network which will provide a sufficiently high level of reliability of detection of intrusions into the computer network. Methodology. As an architectural solution of the attack detection module, a two-level network system is proposed, based on an ensemble of five neural networks of the multilayer perceptron type. The first neural network to determine the category of attack class (DoS, R2L, U2R, Probe) or the fact that there was no attack; other neural networks – to detect the type of attack, if any (each of these four neural networks corresponds to one class of attack and is able to identify types that belong only to this class). Findings. The created software model was used to study the parameters of the neural network configuration 41–1–132–5, which determines the category of the attack class on the computer network. It is determined that the optimal training speed is 0.001. The ADAM algorithm proved to be the best for optimization. The ReLU function is the most suitable activation function for the hidden layer, and the hyperbolic tangent function – for the output layer activation function. Accuracy in test and validation samples was 92.86 % and 91.03 %, respectively. Originality. The developed software model, which uses the Python 3.5 programming language, the integrated development environment PyCharm 2016.3 and the Tensorflow 1.2 framework, makes it possible to detect all types of attacks of DoS, U2R, R2L, Probe classes. Practical value. Graphical dependencies of accuracy of neural networks at various parameters are received: speed of training; activation function; optimization algorithm. The optimal parameters of neural networks have been determined, which will ensure a sufficiently high level of reliability of intrusion detection into a computer network.