Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) was originally founded in 1959. The publisher of the journal is Wuhan University of Technology. JWUT first got the scopus license in the year 2001. The journal generally publishes all aspect of engineering sciences like: physics, chemistry, mathematics, and all sorts of general engineering.
Scopus Indexed(2023)
Submission Deadline
Volume
47
, Issue
04
04 Oct 2023
Day
Hour
Min
Sec
Publish On
Volume
47
, Issue
03
30 Sep 2023
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
Aim and Scopes
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) (ISSN:2095-3844) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are in the following fields but not limited to: :
Electrical Engineering and Telecommunication Section:
Computer Science Section :
Civil and architectural engineering :
Mechanical and Materials Engineering :
kinematics and dynamics of rigid bodies, theory of machines and mechanisms, vibration and balancing of machine parts, stability of mechanical systems, mechanics of continuum, strength of materials, fatigue of materials, hydromechanics, aerodynamics, thermodynamics, heat transfer, thermo fluids, nanofluids, energy systems, renewable and alternative energy, engine, fuels, nanomaterial, material synthesis and characterization, principles of the micro-macro transition, elastic behavior, plastic behavior, high-temperature creep, fatigue, fracture, metals, polymers, ceramics, intermetallics. Kongzhi yu Juece/Control and Decision Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Zhenkong Kexue yu Jishu Xuebao/Journal of Vacuum Science and Technology Zhonghua yi shi za zhi (Beijing, China : 1980) Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science) Teikyo Medical Journal Tobacco Science and Technology Changjiang Liuyu Ziyuan Yu Huanjing/Resources and Environment in the Yangtze Valley Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentationChemical Engineering :
Food Engineering :
Physics Section:
Mathematics Section:
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
Multi-objective Dynamic Stowage Planning Decision for Passenger-cargo RORO Ships Under Online Environment
The passenger-cargo Roll on/Roll off ship stowage (PRSS) is the core step of passengercargo Roll on/Roll off (RoRo) transportation. The layout of vehicles in the cabin is directly related to the space utilization of the cabin and the efficiency of stowage operations, which in turn affects the economic benefits of the port. In this paper, we address the PRSS problem in the context of passenger-cargo RoRo transportation in the Qiongzhou Strait of China. By focusing on the utilization ratio of the cabin area, the PRSS problem can be viewed as a special version of a two-dimensional knapsack packing (2D-KP) problem with additional constraints, such as two-phase, complex rotation and safe navigation constraints. Then we present a mixed integer linear programming (MILP) mathematical model and an algorithm framework to tackle the PRSS problem. In the algorithm framework, a novel multi-phase heuristic stowage method is proposed to improve the current manual stowage decision-making state which completely depends on operational experience. Finally, several instances are generated based on the realistic date of Qiongzhou Strait to verify the effectiveness of the model and stowage method. Computational results show that the proposed model and stowage method are well suited to solve the PRSS problem and the algorithm framework has a strong robustness in large-scale application experiments.
Trajectory Tracking Method of Wheeled AGV Based on Adaptive Backstepping
This paper proposes trajectory tracking algorithm for differential drive type of Automatic Guided Vehicle (AGV) system with the unknown wheel radii using adaptive backstepping control method. To guarantee the tracking errors go to zero, backstepping control method is proposed. By choosing appropriate Lyapunov function based on its kinematic modeling, system stability is guaranteed and a control law can be obtained. In this paper, the unknown radii of left and right wheels caused by uneven load distribution or manufacturing imperfection are considered. To solve this problem, an adaptive law is proposed to estimate the changing of wheels radii. The simulation and experimental results show that the proposed controller successfully estimates the unknown parameters and tracks the reference trajectories.
Asphalt Pavement Texture Image Restoration Method Based on Generative Adversarial Network
A super-resolution reconstruction approach based on an improved generative adversarial network is presented to overcome the huge disparities in image quality due to variable equipment and illumination conditions in the image-collecting stage of intelligent pavement detection. The nonlinear network of the generator is first improved, and the Residual Dense Block (RDB) is created to serve as Batch Normalization (BN). The Attention Module is then formed by combining the RDB, Gated Recurrent Unit (GRU), and Conv Layer. Finally, a loss function based on the L1 norm is utilized to replace the original loss function. The experimental findings demonstrate that the self-built pavement crack dataset’s Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) of the reconstructed images reach 29.21 dB and 0.854, respectively. The results improved compared to the Set5, Set14, and BSD100 datasets. Additionally, by employing Faster-RCNN and a Fully Convolutional Network (FCN), the effects of image reconstruction on detection and segmentation are confirmed. The findings indicate that the segmentation results’ F1 is enhanced by 0.012 to 0.737 and the detection results’ confidence is increased by 0.031 to 0.9102 when compared to state-of-the-art methods. It has a significant engineering application value and can successfully increase pavement crack-detecting accuracy.
Research on Redundant Setting Spacing of Guide Signs Based on Distracted Driving
While driving simulators allow for the examination of a range of driving performance measures in a controlled, relatively realistic and safe driving environment, driver distraction is a multidimensional phenomenon which means that no single driving performance measure can capture all effects of distraction. Furthermore, the large number of driving related outcomes each simulator provides, indicates that the decision regarding which measure or set of measures is used should be guided by specific criteria. The objective of this paper is a comprehensive review of driving performance parameters critical for distracted driving research. For this purpose an extended literature review took place in order to investigate the critical parameters which are examined in the scientific field of driver distraction. Firstly, all driving performance parameters examined in driving simulator experiments are identified and analysed including lateral control, longitudinal control, reaction time, gap acceptance, eye movement and workload measures, while a list of the most common driving simulator dependent variables is cited. Subsequently, a thorough literature review is carried out including 42 studies examining driver distraction through driving simulator experiments which were published in scientific journals, concern recent research and report quantitative results. In this framework, the respective driving performance measures are recorder aiming to investigate which and how they are analysed. A basic remark concerns the quantitative measures used to express driver distraction. In most cases, driver distraction is measured in terms of its impact to driver attention, driver behaviour and driver accident risk. It is noted that the specific measures used vary significantly. However, the diversity in the measures used, in combination with the diversity in the design of the experiments (i.e. road and traffic factors examined, number and duration of trials) often complicates the synthesis of the results, especially for the less commonly examined distraction factors.
Spatial-temporal Characteristics and Driving Factors of Aircraft Carbon Emissions in Airports
This paper analyzes aircraft CO2 emissions (in both quantity and intensity per passenger) during landing and take-off cycles at nine different airports in Jiangsu province (China) over a ten-year time span (2007–2016). Our database is unique and very detailed in that we combine flight schedules, with aircraft type (engines) used, and landing-and-take-off cycles. We are particularly interested in how the spatial characteristics impact emission levels. To this end we estimate a CO2 emission model taking the airport characteristics into account, and apply a spatial classification and autocorrelation model to distinguish between different types of airports and systems. Our analysis shows that: (1) there are strong spatial distribution differences between airports due to the patterns of economic development, airport size and aircraft used; (2) most airports have a high reduction potential of CO2 emission, without a loss of economic performance; (3) significant spatial aggregation effects exist and are persistent during most observational years, which indicates a strong Matthew effect of CO2 emission within Jiangsu province; and (4) airport size, linkage to the local economy, and airport location are closely related to aircraft CO2 emissions. We also provide a number of recommendations to improve airport CO2 emissions and add to sustainable development.
Research on Feature Extraction Method of Marine Diesel Engine Wear Information Based on Kernel Principal Component
Due to heavy work load of marine diesel engines, the failure in their mechanical components may result in serious accidents. Existing condition monitoring methods for marine diesel engines usually adopt warning after the failure occurred. In order to predict potential faults, this work has put forward a remote intelligent monitoring system for marine diesel engines. The global system for mobile communication mode was employed to construct the basis of data remote transmission, and a new multi-kernel extreme learning machine algorithm was proposed to diagnose the early faults in an intelligent method. Experimental tests were carried out in the marine diesel engine fault diagnosis set-up. The analysis results show that the proposed remote intelligent monitoring system can accurately, timely and reliably detect the potential failures. Meanwhile, the proposed multi-kernel extreme learning machine was compared with the existing methods. The comparison indicates that the multi-kernel extreme learning machine outperforms its rivals in term of fault detection rate by an increase of 3.4%. Therefore, the proposed remote intelligent monitoring system has good prospects for engineering applications.
Passenger Flow Prediction Method for Rail Transit Stations Based on Empirical Mode Decomposition and K-nearest Neighbors
It is difficult for a single model to simultaneously capture the nonlinear, correlation, and periodicity of data series in the passenger flow prediction of urban rail transit (URT). To better predict the short-term passenger flow of URT, based on the long short-term memory network (LSTM) model, a deep learning model prediction method combining the time convolution network (TCN) and the long short-term memory network (LSTM) based on machine learning is proposed. The model couples the external factors such as date attributes, weather conditions, and air quality, to improve the overall prediction performance and solve the difficulty of accurate prediction due to the large fluctuation and randomness of short-term passenger flow in rail transit. Using the swiping data and related weather information of some stations of Chongqing Rail Transit Line 3, the TCN-LSTM model is verified by an example, and the prediction results of the single LSTM model are given for comparison. The results show that the TCN-LSTM model can better predict the passenger flow characteristics of different stations at different times. Compared with the single LSTM model, the TCN-LSTM model has better prediction accuracy and data generalization ability.
Study on Equivalent Solution Method of Static and Dynamic Characteristics for Orthogonally Rib-stiffened Sandwich Plate
Free and forced vibrations of Z-reinforced sandwich plates stiffened by steel ribs are researched by experimental and mixed analytical–numerical techniques. A test sample of the plate is firstly manufactured and an underwater vibration test is conducted with a random white-noise force. Meanwhile, a mixed analytical–numerical method (MA-NM) is proposed, which uses an analytical homogenization for the Z-reinforced core and coupling finite element and boundary element (FE/BE) analyses for the plate. Natural frequencies given by the MA-NM are compared with ones from the detailed FE model. Forced vibration responses from the MA-NM are validated against the experimental data. Good agreements are obtained and the high accuracy of the MA-NM is demonstrated. Parameters influences are then analyzed through the MA-NM. The core material shows a larger effect on natural frequencies than the reinforcement material. Ribs at the orthotropic midlines greatly enhance the bend stiffness of the whole structure, and ribs at the surrounding sides mainly influence boundary conditions. When in-plane coordinates of the external force near the plate center, vibration responses have fewer resonance peaks and lower amplitudes, while the influence of the thickness direction coordinate could be ignored. The fluid loading reduces both natural frequencies and vibration responses.
Evaluation Method of Traffic Operation Status of Urban Road Intersections Based on Efficiency Coordination
Signal coordination is perceived by many agencies as an advantageous improvement to the community or corridor in consideration. In many cases, signal coordination techniques have proven to be successful in improving the quality of life and mobility through the area. This study determines the coordination system pattern of traffic signal for four consecutive intersections spaced at 780 m distance. Data for vehicles movement were collected using video camera during morning and evening peak hour with congested conditions. For evaluation of the possible coordination of signalized intersections a simulation model, TRANSYT7F, was used. The results show after coordinating, the amount of delay, travel time, and queue reduce.
Demand Forecast of Floating Bicycle Based on LSTM Network
As a convenient, economical, and eco-friendly travel mode, bike-sharing greatly improved urban mobility. However, it is often very difficult to achieve a balanced utilization of shared bikes due to the asymmetric spatio-temporal user demand distribution and the insufficient numbers of shared bikes, docks, or parking areas. If we can predict the short-run bike-sharing demand, it will help operating agencies rebalance bike-sharing systems in a timely and efficient way. Compared to the statistical methods, deep learning methods can automatically learn the relationship between the inputs and outputs, requiring less assumptions and achieving higher accuracy. This study proposes a Spatial-Temporal Graph Attentional Long Short-Term Memory (STGA-LSTM) neural network framework to predict short-run bike-sharing demand at a station level using multi-source data sets. These data sets include historical bike-sharing trip data, historical weather data, users’ personal information, and land-use data. The proposed model can extract spatio-temporal information of bike-sharing systems and predict the short-term bike-sharing rental and return demand. We use a Graph Convolutional Network (GCN) to mine spatial information and adopt a Long Short-Term Memory (LSTM) network to mine temporal information. The attention mechanism is focused on both temporal and spatial dimensions to enhance the ability of learning temporal information in LSTM and spatial information in GCN. Results indicate that the proposed model is the most accurate compared with several baseline models, the attention mechanism can help improve the model performance, and models that include exogenous variables perform better than the models that only consider historical trip data. The proposed short-term prediction model can be used to help bike-sharing users better choose routes and to help operators implement dynamic redistribution strategies.
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/Journal of Wuhan University of Technology (Transportation Science and Engineering)