Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
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2024, 38(4): 355-362.
doi: 10.12299/jsues.23-0271
Abstract:
Based on a heavy-duty diesel engine, the effects of different ammonia energy ratio (AER) and diesel injection strategies on the combustion performance of ammonia-diesel dual-fuel engine were investigated by using CONVERGE software, the indicated thermal efficiency (ITE) of the engine was optimized by adjusting the diesel injection strategy. The results of the study show that the AER increases from 0% to 80% with a consequent decrease in the ITE, the increase in the reaction product N2O resulted in greater greenhouse gas (GHG) emissions than in the original diesel-only combustion mode of the diesel engine. The AER is constant at 50%. At the optimum point of diesel single injection timing, the ITE is comparable to that of the original diesel combustion mode, while GHG emissions decreases by 19.7% and NO emissions increases by 41.6%. At the optimum point of pre- and main injection timing for diesel split injection, the ITE increases by 1.6%, GHG emission decreases by 40.4%, and NO emission increased by 31.8% compared with the original diesel combustion mode, delaying the main injection timing of diesel split injection can effectively reduce NO emissions.
Based on a heavy-duty diesel engine, the effects of different ammonia energy ratio (AER) and diesel injection strategies on the combustion performance of ammonia-diesel dual-fuel engine were investigated by using CONVERGE software, the indicated thermal efficiency (ITE) of the engine was optimized by adjusting the diesel injection strategy. The results of the study show that the AER increases from 0% to 80% with a consequent decrease in the ITE, the increase in the reaction product N2O resulted in greater greenhouse gas (GHG) emissions than in the original diesel-only combustion mode of the diesel engine. The AER is constant at 50%. At the optimum point of diesel single injection timing, the ITE is comparable to that of the original diesel combustion mode, while GHG emissions decreases by 19.7% and NO emissions increases by 41.6%. At the optimum point of pre- and main injection timing for diesel split injection, the ITE increases by 1.6%, GHG emission decreases by 40.4%, and NO emission increased by 31.8% compared with the original diesel combustion mode, delaying the main injection timing of diesel split injection can effectively reduce NO emissions.
2024, 38(4): 363-369.
doi: 10.12299/jsues.23-0215
Abstract:
Given that the vehicle routing problem with capacity constraints is easily influenced by uncertain factors such as customer location and demand, and leads to the infeasible or non-optimal solution, an improved discrete differential evolution algorithm was proposed. The greedy method was employed to construct the initial solution, thereby enhancing its quality of the initial solution. The mutation operator was redesigned according to the integer permutation characteristics, and the crossover operator was adapted for problem model, which improved the algorithm performance effectively. A local search strategy that incorporates local relocation, conditional exchange and a maximum contribution removal mechanism were designed, enhancing algorithm's exploration capabilities. Simulation experiments on the benchmark test set show that the improved algorithm can find the optimal value for 31 out of 33 test cases, indicating superior solution capabilities compared to other algorithms and effectively addressing the vehicle routing problem with capacity constraints.
Given that the vehicle routing problem with capacity constraints is easily influenced by uncertain factors such as customer location and demand, and leads to the infeasible or non-optimal solution, an improved discrete differential evolution algorithm was proposed. The greedy method was employed to construct the initial solution, thereby enhancing its quality of the initial solution. The mutation operator was redesigned according to the integer permutation characteristics, and the crossover operator was adapted for problem model, which improved the algorithm performance effectively. A local search strategy that incorporates local relocation, conditional exchange and a maximum contribution removal mechanism were designed, enhancing algorithm's exploration capabilities. Simulation experiments on the benchmark test set show that the improved algorithm can find the optimal value for 31 out of 33 test cases, indicating superior solution capabilities compared to other algorithms and effectively addressing the vehicle routing problem with capacity constraints.
2024, 38(4): 370-374.
doi: 10.12299/jsues.24-0010
Abstract:
To address issues related to passenger congestion at the stairs and escalators of the platforms resulting from the intense gathering of passenger flow upon the arrival of rail transit trains, the impedance function of the optimal decision selecting stair and escalator was constructed, which integrated both physical consumption and time consumption. Firstly, the number of passengers disembarking from each door of the arriving train were collected and Dijkstra's algorithm was utilized to allocate passengers to stair and escalator groups. Secondly, passenger queuing parameters at stairs and escalators were calculated. Thirdly, combined with the queuing characteristics, the impedance functions for stair and escalator selection were built, then the optimal decision of stair and escalator selection for different passengers were obtained. Finally, a case study of Hongqiao Railway Station of Shanghai Metro Line 2 was carried out. The results indicated that, when the waiting time in the queue before the escalator was 10.32 s, it was recommended for regular passengers to use the stairs; when the waiting time in the queue before the escalator increased to 29.91 and 40.31 s, it was advisable for passengers with luggage to use the stairs.
To address issues related to passenger congestion at the stairs and escalators of the platforms resulting from the intense gathering of passenger flow upon the arrival of rail transit trains, the impedance function of the optimal decision selecting stair and escalator was constructed, which integrated both physical consumption and time consumption. Firstly, the number of passengers disembarking from each door of the arriving train were collected and Dijkstra's algorithm was utilized to allocate passengers to stair and escalator groups. Secondly, passenger queuing parameters at stairs and escalators were calculated. Thirdly, combined with the queuing characteristics, the impedance functions for stair and escalator selection were built, then the optimal decision of stair and escalator selection for different passengers were obtained. Finally, a case study of Hongqiao Railway Station of Shanghai Metro Line 2 was carried out. The results indicated that, when the waiting time in the queue before the escalator was 10.32 s, it was recommended for regular passengers to use the stairs; when the waiting time in the queue before the escalator increased to 29.91 and 40.31 s, it was advisable for passengers with luggage to use the stairs.
2024, 38(4): 375-381.
doi: 10.12299/jsues.24-0001
Abstract:
Cognitive abilities of 354 metro train drivers were assessed by using the Vienna test system (VTS). An unsupervised learning model was developed through K-means clustering algorithm on the VTS data to establish a cognitive ability classification model. With the maximum Recall value as the objective function, XGBoost training and optimization were performed on the classification model. SHAP algorithm was employed to analyze the importance of various cognitive ability feature indicators in the model, and three key factors that mean reaction time, total correct responses, visual field range, as well as their interactions were identified. The research results can provide a more precise tool for the selection, on-the-job assessment, and training of metro train drivers when applied to the field of cognition and emergency capabilities.
Cognitive abilities of 354 metro train drivers were assessed by using the Vienna test system (VTS). An unsupervised learning model was developed through K-means clustering algorithm on the VTS data to establish a cognitive ability classification model. With the maximum Recall value as the objective function, XGBoost training and optimization were performed on the classification model. SHAP algorithm was employed to analyze the importance of various cognitive ability feature indicators in the model, and three key factors that mean reaction time, total correct responses, visual field range, as well as their interactions were identified. The research results can provide a more precise tool for the selection, on-the-job assessment, and training of metro train drivers when applied to the field of cognition and emergency capabilities.
2024, 38(4): 382-388.
doi: 10.12299/jsues.23-0218
Abstract:
The performance of parallel single-flow channel fuel cells with a rectangular cross-section was investigated using COMSOL software. A performance simulation model was established and its feasibility was verified. On this basis, the impacts of different cross-sectional shapes on the power density, mass transfer, temperature, velocity, pressure drop, and net output efficiency of parallel single channel fuel cells were examined. The result shows that the cross-sectional shape of the flow channel significantly affects the fuel cell performance under low voltage and high current density conditions. The triangular cross-section model generally exhibits a higher maximum power density. The W-shaped cross-section has a higher flow velocity and smaller temperature change compared to the rectangular cross-section, with an increase of 1.8% in hydrogen consumption and a 6.6% in oxygen consumption respectively. It demonstrates a higher power density with a similar net output efficiency, providing support for the design of bipolar plates.
The performance of parallel single-flow channel fuel cells with a rectangular cross-section was investigated using COMSOL software. A performance simulation model was established and its feasibility was verified. On this basis, the impacts of different cross-sectional shapes on the power density, mass transfer, temperature, velocity, pressure drop, and net output efficiency of parallel single channel fuel cells were examined. The result shows that the cross-sectional shape of the flow channel significantly affects the fuel cell performance under low voltage and high current density conditions. The triangular cross-section model generally exhibits a higher maximum power density. The W-shaped cross-section has a higher flow velocity and smaller temperature change compared to the rectangular cross-section, with an increase of 1.8% in hydrogen consumption and a 6.6% in oxygen consumption respectively. It demonstrates a higher power density with a similar net output efficiency, providing support for the design of bipolar plates.
2024, 38(4): 389-394.
doi: 10.12299/jsues.23-0201
Abstract:
In winter, the surface of the vertical fin of the cold end of the outdoor air intake device is prone to frost. Through visualization experiments, the characteristics of frost formation on the surface of vertical fins under different air conditions were studied to provide a basis for suppressing frost formation on the surface of fins. The results show that the frost crystal morphology of fin surface is affected by the cold surface temperature, and the frost crystal show irregular shape, fan shape, feather shape and tree shape in turn when the cold surface temperature decreases. When set the relative humidity of the air to 60% and increase the air temperature within the range of 10 to 25 ℃, it was found that the freezing time of water droplets on the surface of the fin was shortened from 273 to 205 s and then extended to 269 s. The average frost growth rate was first fast and then slow. When the air temperature is setted to 20 ℃ and the relative humidity of air increases within the range of 20% ~ 80%, the freezing time of water droplets on the surface of fin is gradually shortened from 345 to 218 s, and the average growth rate of frost layer is faster. The moisture content of the air is approximately equal, the air temperature increases within the range of 10 to 25 ℃, and the average growth rate of the frost layer slows down.
In winter, the surface of the vertical fin of the cold end of the outdoor air intake device is prone to frost. Through visualization experiments, the characteristics of frost formation on the surface of vertical fins under different air conditions were studied to provide a basis for suppressing frost formation on the surface of fins. The results show that the frost crystal morphology of fin surface is affected by the cold surface temperature, and the frost crystal show irregular shape, fan shape, feather shape and tree shape in turn when the cold surface temperature decreases. When set the relative humidity of the air to 60% and increase the air temperature within the range of 10 to 25 ℃, it was found that the freezing time of water droplets on the surface of the fin was shortened from 273 to 205 s and then extended to 269 s. The average frost growth rate was first fast and then slow. When the air temperature is setted to 20 ℃ and the relative humidity of air increases within the range of 20% ~ 80%, the freezing time of water droplets on the surface of fin is gradually shortened from 345 to 218 s, and the average growth rate of frost layer is faster. The moisture content of the air is approximately equal, the air temperature increases within the range of 10 to 25 ℃, and the average growth rate of the frost layer slows down.
2024, 38(4): 395-399.
doi: 10.12299/jsues.23-0272
Abstract:
Titanium alloy has low density, high specific strength, high stiffness, high temperature resistance and corrosion resistance. However, its low hardness, poor thermal conductivity, high viscosity and poor wear resistance have become an important problem restricting the application of titanium alloys. Graphene reinforced titanium matrix composites were prepared by powder metallurgy, the effects of graphene on microstructure, hardness, tensile properties and friction and wear properties were studied. The results show that the grain size of titanium matrix composites is smaller than that of matrix, and the grain size decreases with the increase of graphene content. Compared with the matrix, the hardness, strength and friction and wear properties of titanium matrix composites are significantly improved. With the increasing of graphene content, the hardness and strength increase, the plasticity decreases, and the friction coefficient and wear amount both decrease. When the sintering temperature is1273 K and the mass fraction of graphene is 0.5%, the hardness, tensile and friction wear properties of titanium matrix composites are the best, and the wear loss is the lowest, which is 40% lower than that of matrix.
Titanium alloy has low density, high specific strength, high stiffness, high temperature resistance and corrosion resistance. However, its low hardness, poor thermal conductivity, high viscosity and poor wear resistance have become an important problem restricting the application of titanium alloys. Graphene reinforced titanium matrix composites were prepared by powder metallurgy, the effects of graphene on microstructure, hardness, tensile properties and friction and wear properties were studied. The results show that the grain size of titanium matrix composites is smaller than that of matrix, and the grain size decreases with the increase of graphene content. Compared with the matrix, the hardness, strength and friction and wear properties of titanium matrix composites are significantly improved. With the increasing of graphene content, the hardness and strength increase, the plasticity decreases, and the friction coefficient and wear amount both decrease. When the sintering temperature is
2024, 38(4): 400-405.
doi: 10.12299/jsues.23-0229
Abstract:
To investigate reasons for the impact of manufacturing defects on bearing performance of carbon fiber aluminum honeycomb sandwich structures, flat compression tests and three-point bending tests under quasi-static conditions were conducted on these structures with glued joints, the different forms of debonding failure existing in the same batch and size were analyzed. The trends of peak load and average load generated by the sandwich structure were observed and compared with the load-displacement curves of non-debonded specimens simulated using Abaqus CAE software, to study the changes in its bearing capacity. The results show that sandwich structures with manufacturing defects exhibit different forms of debonding and have lower bearing capacity than non-debonded specimens, which can provide a basis for improving the fabrication process and have great significance for broadening its application ares.
To investigate reasons for the impact of manufacturing defects on bearing performance of carbon fiber aluminum honeycomb sandwich structures, flat compression tests and three-point bending tests under quasi-static conditions were conducted on these structures with glued joints, the different forms of debonding failure existing in the same batch and size were analyzed. The trends of peak load and average load generated by the sandwich structure were observed and compared with the load-displacement curves of non-debonded specimens simulated using Abaqus CAE software, to study the changes in its bearing capacity. The results show that sandwich structures with manufacturing defects exhibit different forms of debonding and have lower bearing capacity than non-debonded specimens, which can provide a basis for improving the fabrication process and have great significance for broadening its application ares.
2024, 38(4): 406-413.
doi: 10.12299/jsues.23-0254
Abstract:
A rolling bearing fault diagnosis method based on adaptive spectrum loss generative adversarial networks was proposed. Firstly, the spectral distance was introduced to measure the frequency domain difference between generated data and real data. Secondly, adaptive spectrum loss was added to the generator loss to reduce the weight of simple frequency components, so as toadaptively focus on difficult-to-synthesize frequency components, to better guide the generative adversarial networks to generate fake samples more similar to real data. The proposed method was validated using the Case Western Reserve University bearing dataset. Compared with other methods, the adaptive spectrum loss generative adversarial networks can generate higher-quality samples and significantly improve fault recognition rate under sample imbalance conditions.
A rolling bearing fault diagnosis method based on adaptive spectrum loss generative adversarial networks was proposed. Firstly, the spectral distance was introduced to measure the frequency domain difference between generated data and real data. Secondly, adaptive spectrum loss was added to the generator loss to reduce the weight of simple frequency components, so as toadaptively focus on difficult-to-synthesize frequency components, to better guide the generative adversarial networks to generate fake samples more similar to real data. The proposed method was validated using the Case Western Reserve University bearing dataset. Compared with other methods, the adaptive spectrum loss generative adversarial networks can generate higher-quality samples and significantly improve fault recognition rate under sample imbalance conditions.
2024, 38(4): 414-421.
doi: 10.12299/jsues.23-0217
Abstract:
A short-term wind power prediction method based on mRMR and SVMD-TPA-BiLSTM was proposed. Firstly, successive variational modal decomposition (SVMD) was adopted to reduce the dimensionality of wind power sequences, and the decomposed subsequences were combined with the key meteorological feature data selected maximum correlation minimum redundancy (mRMR) to form a training set. Secondly, a combined model based on the temporal pattern attention (TPA) mechanism to improve the Bi-directional long short-term memory neural network (BiLSTM) was established, and TPA mechanism was used to capture the correlation of different time sequence data. Finally, the prediction component results were superimposed to obtain the final prediction results. Taking the dataset of a certain wind farm as an example, different models were subjected to single step and multi-step prediction experiments to compare the accuracy, and it was proved that the prediction method can effectively portray the component characteristics of wind power and improve the prediction accuracy of wind power.
A short-term wind power prediction method based on mRMR and SVMD-TPA-BiLSTM was proposed. Firstly, successive variational modal decomposition (SVMD) was adopted to reduce the dimensionality of wind power sequences, and the decomposed subsequences were combined with the key meteorological feature data selected maximum correlation minimum redundancy (mRMR) to form a training set. Secondly, a combined model based on the temporal pattern attention (TPA) mechanism to improve the Bi-directional long short-term memory neural network (BiLSTM) was established, and TPA mechanism was used to capture the correlation of different time sequence data. Finally, the prediction component results were superimposed to obtain the final prediction results. Taking the dataset of a certain wind farm as an example, different models were subjected to single step and multi-step prediction experiments to compare the accuracy, and it was proved that the prediction method can effectively portray the component characteristics of wind power and improve the prediction accuracy of wind power.
2024, 38(4): 422-428.
doi: 10.12299/jsues.23-0262
Abstract:
A wearable flexible supernumerary robotic finger system capable of collaborative grasping with disabled arms was proposed, and its structure and prototype were designed. A kinematic model for supernumerary robotic finger bending and grasping objects was established, the forward and inverse kinematic mapping relationship between the stretching displacement of the driving rope, the joint angle of the finger joint, and the pose of the end finger joint were obtained. The dynamics of the flexible supernumerary robotic fingers were carried out through simulation, the movement rules of the unloaded finger end were obtained. Through experimental construction, the flexible supernumerary robotic finger prototype can achieve stable envelope grasping of objects 1.7 times larger than the body. It verifies the rationality of kinematic modeling and structural design, and has certain practicality.
A wearable flexible supernumerary robotic finger system capable of collaborative grasping with disabled arms was proposed, and its structure and prototype were designed. A kinematic model for supernumerary robotic finger bending and grasping objects was established, the forward and inverse kinematic mapping relationship between the stretching displacement of the driving rope, the joint angle of the finger joint, and the pose of the end finger joint were obtained. The dynamics of the flexible supernumerary robotic fingers were carried out through simulation, the movement rules of the unloaded finger end were obtained. Through experimental construction, the flexible supernumerary robotic finger prototype can achieve stable envelope grasping of objects 1.7 times larger than the body. It verifies the rationality of kinematic modeling and structural design, and has certain practicality.
2024, 38(4): 429-436, 464.
doi: 10.12299/jsues.24-0007
Abstract:
To address the problem of time-consuming and large errors in the attitude PID parameter tuning of a high-rise curtain wall cleaning robot, an improved genetic algorithm (IGA) was proposed. The halton sequence was introduced as the initial population, the adaptive dynamic regulation mechanism was introduced in the crossover and variation stages, and improved integral of time-weighted absolute error (IITAE) evaluation index function with better comprehensive performance was proposed. PID parameter tuning experiments were carried out using IGA, manual empirical method, AGPSO and GA. The results show that the tuning results of the IGA algorithm are more than 20% higher than other algorithms in terms of the objective function value and reduced the convergence time by more than 50%. The controller designed by the IGA methodcan achieve stable control of robot attitude, which has good application value for air attitude stability control of curtain wall cleaning robot.
To address the problem of time-consuming and large errors in the attitude PID parameter tuning of a high-rise curtain wall cleaning robot, an improved genetic algorithm (IGA) was proposed. The halton sequence was introduced as the initial population, the adaptive dynamic regulation mechanism was introduced in the crossover and variation stages, and improved integral of time-weighted absolute error (IITAE) evaluation index function with better comprehensive performance was proposed. PID parameter tuning experiments were carried out using IGA, manual empirical method, AGPSO and GA. The results show that the tuning results of the IGA algorithm are more than 20% higher than other algorithms in terms of the objective function value and reduced the convergence time by more than 50%. The controller designed by the IGA methodcan achieve stable control of robot attitude, which has good application value for air attitude stability control of curtain wall cleaning robot.
2024, 38(4): 437-443.
doi: 10.12299/jsues.23-0245
Abstract:
In order to accurately control the surface skin temperature of moxibustion and improve the therapeutic effect of moxibustion, an intelligent moxibustion temperature control system was designed based on a moxibustion robot. The fuzzy PID control algorithm was used to stably control the surface skin temperature of moxibustion. Simulation result shows that the system temperature error always fluctuates within ± 0.4 ℃, and the maximum overshoot is less than 1.3 ℃. Compared with the conventional PID control system, the intelligent moxibustion temperature control system based on the fuzzy PID has smaller temperature overshoot, faster stabilization time, better temperature control accuracy, and can improve patient comfort.
In order to accurately control the surface skin temperature of moxibustion and improve the therapeutic effect of moxibustion, an intelligent moxibustion temperature control system was designed based on a moxibustion robot. The fuzzy PID control algorithm was used to stably control the surface skin temperature of moxibustion. Simulation result shows that the system temperature error always fluctuates within ± 0.4 ℃, and the maximum overshoot is less than 1.3 ℃. Compared with the conventional PID control system, the intelligent moxibustion temperature control system based on the fuzzy PID has smaller temperature overshoot, faster stabilization time, better temperature control accuracy, and can improve patient comfort.
2024, 38(4): 444-450.
doi: 10.12299/jsues.24-0012
Abstract:
Deep learning techniques can assist doctors in precise tumor segmentation. However, existing methods often suffer from issues such as fuzzy segmentation edges and large model parameter counts due to the unclear boundaries between lung tumors and surrounding tissues. A partial convolution coordinate attention U-net (PCU-Net) algorithm for lightweight lung tumor segmentation was proposed. The partial convolution was introduced to reduce model parameters and enhance feature extraction capability. The coordinate attention module was added at skip connection of PCU-Net, so that more precise localization of tumors was achieved by network and segmentation accuracy was improved. The research result shows that the improved PCU-Net can reduce model parameters by 58.57% while increase Dice coefficient, Intersection over Union (IoU) and Recall by 4.22%, 4.26% and 6.82%, respectively. The comparison between PPU-Net and other semantic segmentation models shows that Dice coefficient of PCU-Net is 3-6 percentage points higher than that of other models.
Deep learning techniques can assist doctors in precise tumor segmentation. However, existing methods often suffer from issues such as fuzzy segmentation edges and large model parameter counts due to the unclear boundaries between lung tumors and surrounding tissues. A partial convolution coordinate attention U-net (PCU-Net) algorithm for lightweight lung tumor segmentation was proposed. The partial convolution was introduced to reduce model parameters and enhance feature extraction capability. The coordinate attention module was added at skip connection of PCU-Net, so that more precise localization of tumors was achieved by network and segmentation accuracy was improved. The research result shows that the improved PCU-Net can reduce model parameters by 58.57% while increase Dice coefficient, Intersection over Union (IoU) and Recall by 4.22%, 4.26% and 6.82%, respectively. The comparison between PPU-Net and other semantic segmentation models shows that Dice coefficient of PCU-Net is 3-6 percentage points higher than that of other models.
High-quality dynamic real-time rendering method based on conditional generative adversarial networks
2024, 38(4): 451-457.
doi: 10.12299/jsues.24-0015
Abstract:
Focusing on the challenge of real-time rendering in computer graphics, integrating rasterization techniques with optimized conditional generative adversarial networks (CGANs), real-time generation of approximate ray-traced images was achieved, the issue of discontinuity between frames in existing research was effectively addressed, and optimized balances among real-time performance, realism, and visual coherence were achieved. Based on Pix2PixGAN architecture, the structure, data input and loss functions of CGANs were improved, a training rendering dataset using by Unity and Blender was constructed. Experimental results demonstrate that our rendering method can surpass traditional approaches in key performance metrics, enhance the quality of image generation and the coherence between frames.
Focusing on the challenge of real-time rendering in computer graphics, integrating rasterization techniques with optimized conditional generative adversarial networks (CGANs), real-time generation of approximate ray-traced images was achieved, the issue of discontinuity between frames in existing research was effectively addressed, and optimized balances among real-time performance, realism, and visual coherence were achieved. Based on Pix2PixGAN architecture, the structure, data input and loss functions of CGANs were improved, a training rendering dataset using by Unity and Blender was constructed. Experimental results demonstrate that our rendering method can surpass traditional approaches in key performance metrics, enhance the quality of image generation and the coherence between frames.
2024, 38(4): 458-464.
doi: 10.12299/jsues.24-0018
Abstract:
Based on the temperature observation of a kilometer-scale rail-cum-road bridge, temperature distribution characteristics of main girder steel trusses at different locations in different seasons were investigated, and the temporal and spatial temperature field characteristics of the kilometer-scale rail-cum-road bridge were obtained. Based on variation characteristics of the structure and atmospheric temperature, a comprehensive analysis of temperature field of a kilometer-scale rail-cum-road bridge was conducted. The most suitable model for accurate prediction of bridge temperature data was found by comparing three neural network prediction models. The results show that the temperature field of steel trusses have significant difference in time and space; the temperature of main girder rises more slowly than atmospheric temperature; LSTM and CNN neural networks can predict the temperature data with high accuracy.
Based on the temperature observation of a kilometer-scale rail-cum-road bridge, temperature distribution characteristics of main girder steel trusses at different locations in different seasons were investigated, and the temporal and spatial temperature field characteristics of the kilometer-scale rail-cum-road bridge were obtained. Based on variation characteristics of the structure and atmospheric temperature, a comprehensive analysis of temperature field of a kilometer-scale rail-cum-road bridge was conducted. The most suitable model for accurate prediction of bridge temperature data was found by comparing three neural network prediction models. The results show that the temperature field of steel trusses have significant difference in time and space; the temperature of main girder rises more slowly than atmospheric temperature; LSTM and CNN neural networks can predict the temperature data with high accuracy.
2024, 38(4): 465-470.
doi: 10.12299/jsues.24-0219
Abstract:
A gradient boosting decision tree (BDTG) model based on high-energy physics data analysis in ROOT framework was proposed for the identification of heart disease using a multivariate analysis method. Through a large amount of clinical data, the aim is to analyze the various complex relationships of variables to improve the accuracy and reliability of heart disease differentiation. Using the Kaggle open-source heart disease dataset, the results showed that the model did not exhibit any erroneous discrimination when the BDTG responsevalues range between between −0.4 and 0.5. In addition, when the truncation of BDTG response values is −0.6 or 0.6, the model still maintained above 98% in accuracy, precision, recall and F1 scores. Therefore, the model has high accuracy and reliability in the diagnosis of heart disease. This study not only provides new perspectives and methods for predicting heart disease, but also serves as a reference for machine learning prediction research on other diseases.
A gradient boosting decision tree (BDTG) model based on high-energy physics data analysis in ROOT framework was proposed for the identification of heart disease using a multivariate analysis method. Through a large amount of clinical data, the aim is to analyze the various complex relationships of variables to improve the accuracy and reliability of heart disease differentiation. Using the Kaggle open-source heart disease dataset, the results showed that the model did not exhibit any erroneous discrimination when the BDTG responsevalues range between between −0.4 and 0.5. In addition, when the truncation of BDTG response values is −0.6 or 0.6, the model still maintained above 98% in accuracy, precision, recall and F1 scores. Therefore, the model has high accuracy and reliability in the diagnosis of heart disease. This study not only provides new perspectives and methods for predicting heart disease, but also serves as a reference for machine learning prediction research on other diseases.
2017, 31(2): 174-177,182.
doi: 10.3969/j.issn.1009-444X.2017.02.016
摘要：
随着纺织印染行业的不断发展,纺织印染废水成分也日益复杂,大大增加了废水处理难度.针对纺织印染废水的来源和水质特点,综述了国内外纺织印染废水处理技术的研究进展,包括物理处理法、化学处理法和生物处理法,分析了各类处理方法的机制、处理效果及其优缺点.对纺织印染废水处理技术的发展前景进行了展望,指出优化组合废水处理工艺可提高纺织印染废水处理效果及综合回用率.
随着纺织印染行业的不断发展,纺织印染废水成分也日益复杂,大大增加了废水处理难度.针对纺织印染废水的来源和水质特点,综述了国内外纺织印染废水处理技术的研究进展,包括物理处理法、化学处理法和生物处理法,分析了各类处理方法的机制、处理效果及其优缺点.对纺织印染废水处理技术的发展前景进行了展望,指出优化组合废水处理工艺可提高纺织印染废水处理效果及综合回用率.
2018, 32(1): 64-67.
doi: 10.3969/j.issn.1009-444X.2018.01.014
摘要：
交通数据质量是影响交通控制方法有效实施的关键因素之一.为进一步提升交通数据的准确性和时效性,对比分析交通预处理方法,采用标准差公式对拉依达准则进行优化,设计一种适用于交通异常数据检测和修复的优化方法,并结合上海城市快速路实际数据对模型有效性和时效性进行检验.结果表明,基于拉依达准则的数据处理优化方法能够有效、实时地检测交通异常数据、改善数据质量,为道路交通状况的监测和预警提供数据支持.
交通数据质量是影响交通控制方法有效实施的关键因素之一.为进一步提升交通数据的准确性和时效性,对比分析交通预处理方法,采用标准差公式对拉依达准则进行优化,设计一种适用于交通异常数据检测和修复的优化方法,并结合上海城市快速路实际数据对模型有效性和时效性进行检验.结果表明,基于拉依达准则的数据处理优化方法能够有效、实时地检测交通异常数据、改善数据质量,为道路交通状况的监测和预警提供数据支持.
2020, 34(3): 238-246.
doi: 10.3969/j.issn.1009-444X.2020.03.006
摘要：
针对机械臂关节空间轨迹规划的时间优化问题,结合机械臂运动约束,提出基于非线性动态改变惯性权重的粒子群优化(NPSO)算法.根据传统3-5-3多项式插值方法,采用改进粒子群算法寻求最短关节运动时间,研究线性递减改变惯性权重(LPSO)算法和NPSO算法的性能,选用NPSO算法完成关节运动时间最优求解.研究结果显示,经时间优化后的3-5-3插值曲线连续光滑且具备更好的运动特性,整体运动时间缩短约26%,证实提出的方法具有可行性.
针对机械臂关节空间轨迹规划的时间优化问题,结合机械臂运动约束,提出基于非线性动态改变惯性权重的粒子群优化(NPSO)算法.根据传统3-5-3多项式插值方法,采用改进粒子群算法寻求最短关节运动时间,研究线性递减改变惯性权重(LPSO)算法和NPSO算法的性能,选用NPSO算法完成关节运动时间最优求解.研究结果显示,经时间优化后的3-5-3插值曲线连续光滑且具备更好的运动特性,整体运动时间缩短约26%,证实提出的方法具有可行性.
2017, 31(4): 371-375.
doi: 10.3969/j.issn.1009-444X.2017.04.017
摘要：
老年人参与各项社会活动,能够延缓衰老,促进身体健康、提高生活质量.通过开展调研探究了老年人的社会参与意愿及其影响因素.调查发现,城市老年人对经济、政治、社区、文化等各项活动的参与意愿都较高,但是多数的参与停留在被动层面;年龄、性别、文化程度、健康状况等因素会影响参与意愿,且不同活动受影响的程度不同;参与意愿会受到现实条件的约束,如照看小孩,料理家务等.
老年人参与各项社会活动,能够延缓衰老,促进身体健康、提高生活质量.通过开展调研探究了老年人的社会参与意愿及其影响因素.调查发现,城市老年人对经济、政治、社区、文化等各项活动的参与意愿都较高,但是多数的参与停留在被动层面;年龄、性别、文化程度、健康状况等因素会影响参与意愿,且不同活动受影响的程度不同;参与意愿会受到现实条件的约束,如照看小孩,料理家务等.
Numerical Simulation Analysis on Air Distribution of Inter-Column Air Conditioning in a Machine Room
2018, 32(3): 244-249.
doi: 10.3969/j.issn.1009-444X.2018.03.009
摘要：
以上海某数据中心机房为研究对象,针对机房服务器散热量大的特点及传统封闭冷通道存在的问题,提出适合该机房的封闭热通道方案.采用计算流体动力学(CFD)模拟的方法,分析机房内封闭热通道与传统封闭冷通道的温度场和速度场分布特性.通过模拟分析得出:大散热量机房列间空调采用封闭热通道形式效果更佳.
以上海某数据中心机房为研究对象,针对机房服务器散热量大的特点及传统封闭冷通道存在的问题,提出适合该机房的封闭热通道方案.采用计算流体动力学(CFD)模拟的方法,分析机房内封闭热通道与传统封闭冷通道的温度场和速度场分布特性.通过模拟分析得出:大散热量机房列间空调采用封闭热通道形式效果更佳.
2021, 35(3): 237-242.
摘要：
针对传统光流法处理视频序列时存在运行效率低及跟踪偏移问题，结合粒子滤波模型提出一种改进光流法的视频目标跟踪技术. 该技术首先通过遍历法搜索运动点，采用质心定位方式捕获目标质心坐标，然后将得到的视频序列进行光流处理，最后经粒子滤波求解质心运动信息，以实现对视频中目标的精确检测与追踪. 在不同场景下对不同数量、不同类型目标进行仿真跟踪试验，并与光流法、ViBe算法及YOLO算法进行比较. 仿真结果表明，该跟踪技术可使目标跟踪的精准率有效提高5.2%，跟踪效率提高13.7%，同时表现出较好的鲁棒性.
针对传统光流法处理视频序列时存在运行效率低及跟踪偏移问题，结合粒子滤波模型提出一种改进光流法的视频目标跟踪技术. 该技术首先通过遍历法搜索运动点，采用质心定位方式捕获目标质心坐标，然后将得到的视频序列进行光流处理，最后经粒子滤波求解质心运动信息，以实现对视频中目标的精确检测与追踪. 在不同场景下对不同数量、不同类型目标进行仿真跟踪试验，并与光流法、ViBe算法及YOLO算法进行比较. 仿真结果表明，该跟踪技术可使目标跟踪的精准率有效提高5.2%，跟踪效率提高13.7%，同时表现出较好的鲁棒性.
2017, 31(1): 90-94.
doi: 10.3969/j.issn.1009-444X.2017.01.019
摘要：
针对我国黄金期货价格预测问题,对影响我国黄金期货价格的5项指标进行灰色关联度分析,得出我国黄金期货价格与美国黄金期货价格之间的关联度最高.建立反向传播(BP)神经网络模型对我国黄金期货价格预测,并与GM(1,1)方法和ARIMA(0,2,1)模型下的预测结果进行对比.结果显示:与后两个模型相比,BP神经网络模型在黄金期货价格预测方面的精确性更高,具有较好的实用价值.
针对我国黄金期货价格预测问题,对影响我国黄金期货价格的5项指标进行灰色关联度分析,得出我国黄金期货价格与美国黄金期货价格之间的关联度最高.建立反向传播(BP)神经网络模型对我国黄金期货价格预测,并与GM(1,1)方法和ARIMA(0,2,1)模型下的预测结果进行对比.结果显示:与后两个模型相比,BP神经网络模型在黄金期货价格预测方面的精确性更高,具有较好的实用价值.
2018, 32(3): 214-220.
doi: 10.3969/j.issn.1009-444X.2018.03.004
摘要：
介绍功能纺织品的分类,分析功能纺织品的开发途径,主要阐述国内现代功能纺织品研究及开发进展,包括纳米光触媒多功能纺织品、防电磁辐射纺织品、超疏水多功能纺织品、阻燃纺织品、防蚊虫纺织品,并指出采用高新技术开发生态安全高附加值多功能纺织品是功能性纺织品的发展趋势.
介绍功能纺织品的分类,分析功能纺织品的开发途径,主要阐述国内现代功能纺织品研究及开发进展,包括纳米光触媒多功能纺织品、防电磁辐射纺织品、超疏水多功能纺织品、阻燃纺织品、防蚊虫纺织品,并指出采用高新技术开发生态安全高附加值多功能纺织品是功能性纺织品的发展趋势.
2019, 33(3): 215-218,277.
doi: 10.3969/j.issn.1009-444X.2019.03.005
摘要：
综述了二氧化硅(SiO2)薄膜的制备方法和研究进展,介绍了磁控溅射、溶胶凝胶法和真空镀等SiO2薄膜制备方法及其优缺点,论述SiO2薄膜在光学、电学和光电等性能研究方面的新进展,最后对SiO2薄膜的应用和发展进行了展望.
综述了二氧化硅(SiO2)薄膜的制备方法和研究进展,介绍了磁控溅射、溶胶凝胶法和真空镀等SiO2薄膜制备方法及其优缺点,论述SiO2薄膜在光学、电学和光电等性能研究方面的新进展,最后对SiO2薄膜的应用和发展进行了展望.
2017, 31(1): 20-24.
doi: 10.3969/j.issn.1009-444X.2017.01.005
摘要：
介绍了水性工业涂料用丙烯酸酯、聚氨酯、环氧和醇酸4种树脂水性化的最新研究进展.对水性工业涂料的成膜机制进行了总结,并对水分散型、水稀释型等工业涂料的成膜机制做出进一步探讨.总结了水性工业涂料在应用过程中出现的问题并提出解决办法,展望了工业涂料水性化技术面临的挑战与机遇.
介绍了水性工业涂料用丙烯酸酯、聚氨酯、环氧和醇酸4种树脂水性化的最新研究进展.对水性工业涂料的成膜机制进行了总结,并对水分散型、水稀释型等工业涂料的成膜机制做出进一步探讨.总结了水性工业涂料在应用过程中出现的问题并提出解决办法,展望了工业涂料水性化技术面临的挑战与机遇.
2003, 17(2): 83-86.
doi: 10.3969/j.issn.1009-444X.2003.02.001
Abstract:
2019, 33(3): 283-284,288.
doi: 10.3969/j.issn.1009-444X.2019.03.017
Abstract:
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Chinese Quarterly
Supervisor: Shanghai Municipal Education Commission
Sponsors by: Shanghai University of Engineering Science
Publisher: Editorial Department of Journal of Shanghai University of Engineering Science
Editor-in-Chief: WANG Yansong
Email: xuebao@sues.edu.cn
ISSN1009-444X
CN31-1598/T
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