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Productivity analysis of trailing suction hopper dredgers using stacking strategy

AUTOMATION IN CONSTRUCTION 2021年 122卷 103470-103470页

作者： Bai, Shuo Li, Mingchao Song, Lingguang Ren, Qiubing Qin, Liang Fu, Jiake Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300350 Peoples R China Univ Houston Dept Construct Management Houston TX 77204 USA China Commun Construct Co Ltd Tianjin Dredging Co Ltd Tianjin 300042 Peoples R China

Trailing Suction Hopper Dredger (TSHD) is commonly used in dredging construction operations. Accurate productivity estimation and optimization are important for an efficient and smooth operation of TSHDs. An intelligent approach using stacking strategy for estimating TSHD productivity is proposed in this study. The proposed method involves two major modeling stages. First, the ReliefF-Granger algorithm is used to analyze data collected during real-world operation in order to extract the key factors influencing productivity from a vast amount of monitoring data. Several Artificial Intelligence (AI) algorithms were subsequently applied to fuse the extracted factors in order to achieve better generalization capability. In the second stage, a variety of heterogeneous AI models were adopted for mixed feature training prediction, and an optimal combined model was obtained through a grid search algorithm. To verify its accuracy and applicability, the proposed method was applied to a channel deepening project as a case study.

关键词： Trailing suction hopper dredger (TSHD) Dredging operation Productivity prediction Stacking strategy intelligent algorithms

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Comparative Analysis of Intelligence Optimization algorithms in the Thermo-Economic Performance of an Energy Recovery System Based on Organic Rankine Cycle

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JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME 2021年第11期143卷 112101-112101页

作者： Duarte-Forero, Jorge Obregon-Quinones, Luis Valencia-Ochoa, Guillermo Univ Atlantico Dept Mech Engn Barranquilla 080001 Colombia Univ Atlantico Dept Chem Engn Barranquilla 080001 Colombia

This paper compares the performance of a group of intelligent algorithms such as the genetic algorithm (GA), particle swarm optimization (PSO), and repulsive particle swarm optimization (RPSO) based on the optimization of thermo-economic indicators such as the payback period (PBP), the levelized energy cost (LEC), the specific investment cost (SIC), and also in the optimization of the thermodynamic process (net power output) of an energy recovery system in a 2 MW natural gas internal combustion engine based on an organic Rankine cycle. Four parameters were considered to analyze and compare the performance of these algorithms: integral of squared error (ISE), integral of absolute error (IAE), integral of time-weighted absolute error (ITAE), and the integral of time-weighted squared error (ITSE). Analyses of variances (ANOVA) were proposed for each of the parameters studied. The PSO and RPSO algorithms presented the best performance in terms of the mean and the standard deviation of the ISE, IAE, ITAE, and ITSE parameters. Significant differences were not found between the three algorithms in terms of the parameters considered. However, significant differences did exist when comparing groups (pairs) of algorithms considering a significance level of 5%. The ANOVA analysis showed that ITAE was the most affected parameter by population size, while the IAE and ITSE parameters were the less affected. In the optimization, the PSO algorithm obtained the best performance in terms of convergence with values of 0.1110 USD/kWh (LCOE), 4.6971 years (PBP), 1114 USD/kWh (SIC), and 173.64 kW (W-net). PSO-based algorithms obtained better performance in computational terms compared with the genetic algorithms.

关键词： heat recovery system indicators intelligent algorithms organic Rankine cycle thermo-economic energy conversion systems energy systems analysis heat energy generation storage transfer

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Coordinated control of wayside supercapacitor and on-board supercapacitor based on threshold curve optimization in urban rail transit 17

Coordinated control of wayside supercapacitor and on-board s...

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17th IEEE Vehicle Power and Propulsion Conference (VPPC)

作者： Zhong, Zhihong Yang, Zhongping Lin, Fei Fang, Xiaochun

ISBN: (纸本)9781728189598

In order to absorb regenerative braking energy, supercapacitors are widely used in railway. In order to improve the energy storage efficiency, the adjustment of the charge and discharge threshold is important. In this paper, intelligent algorithms and rule-based algorithms are combined. Firstly, charge and discharge threshold curves of wayside and on-hoard supercapacitor are optimized;then, we analyzed the optimized curves in detail and extracted the rules;finally, based on the extracted rules, a corresponding online optimization strategy for charge and discharge thresholds is proposed. After simulation comparison and verification, the energy storage efficiency of the strategy is very well.

关键词： supercapacitor coordinated control intelligent algorithms rules

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Simulating urban growth affected by national and regional land use policies: Case study from Wuhan, China

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LAND USE POLICY 2022年 112卷 105850-105850页

作者： Huang, Xinxin Wang, Haijun Xiao, Fentao Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Peoples R China Wuhan Univ Key Lab Geog Informat Syst MOE Wuhan 430079 Peoples R China Wuhan Urban Construct Grp 9 Changqing Rd Wuhan 430022 Peoples R China

The urban problems have clearly incorporated into the global sustainable development agenda and the scientific urban expansion model has won credibility for urban research and policy evaluation. From a comprehensive perspective, there is still little research on land use policy adjustments facing the potential impact of urbanization. Six different data scenarios, combined with three methods, were used to simulate the temporal and spatial characteristics of urban land use from historical to future 40 years in the context of appropriate data. The results from our study confirm the direct iterate in the initial data. Furthermore, for the long-term simulation of Wuhan, traditional methods perform better than smart methods when simulating regional urban growth changes high precision in continuous iteration for a long period was identified in CAmod1, and the CAmod3 has the highest simulation accuracy in the short-term phase. The filling type was mainly on both sides of the Yangtze River (developed area), while, the edge expansion type was mainly around the administrative boundary of the study area. We also found that the development policies in China were biased towards coastal areas in the early stage. The increase of urban land in Wuhan was slightly lagging behind. Under the control of national and local policies, a significant increase (1995-2000), and only 0.01% increase (2000-2010) in urban area development were observed in Wuhan City, China. According to the forecast results, urban land will grow by 539.73 km2 in 2035, reaching up to 719.76 km2. Therefore, it is necessary to combine multiple methods to collect suggestions in urban planning and to include the factor of policy reference when predicting the changes in future. Reasonable urbanization not only effectively improves the land utilization rate, but also promotes the harmonious and sustainable development of regional economic society.

关键词： Urban land growth intelligent algorithms Cellular automata Long and short term Brainstorming policies Wuhan

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Predictive Modeling of Surface Wear in Mechanical Contacts under Lubricated and Non-Lubricated Conditions

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SENSORS 2021年第4期21卷 1160-1160页

作者： Rahman, Ali Khan, Muhammad Mushtaq, Aleem Tech Univ Darmstadt Dept Elect Engn & Informat Technol D-64283 Darmstadt Germany Cranfield Univ Sch Aerosp Transport & Mfg Cranfield MK43 0AL Beds England Natl Univ Sci & Technol Pakistan Navy Engn Coll Dept Elect & Power Engn Karachi 75350 Pakistan

The surface wear in mechanical contacts under running conditions is always a challenge to quantify. However, the inevitable relationship between the airborne noise and the surface wear can be used to predict the latter with good accuracy. In this paper, a predictive model has been derived to quantify surface wear by using airborne noise signals collected at a microphone. The noise was generated from a pin on disc setup on different dry and lubricated conditions. The collected signals were analyzed, and spectral features estimated from the measurements and regression models implemented in order to achieve an average wear prediction accuracy of within 1mm(3).

关键词： non-contact sensing sensor measurement intelligent algorithms lubrication contact wear noise

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Ambient intelligence in a smart classroom for assessing students' engagement levels

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第10期10卷 3847-3852页

作者： Kim, Pyoung Won Incheon Natl Univ Coll Educ Dept Korean Language Educ 12 Gaetbeol Ro Incheon South Korea

The levels of student engagement refers to the degree to which students are immersed in learning when they are being taught in class. This paper deals with an ambient intelligence algorithm for a smart classroom;the algorithm provides information to the teacher by measuring the level of student engagement in real time. In this study, the algorithm for assessing student engagement levels has been presented;it evaluates the psychological states of students by measuring a thermal infrared image. The algorithm proposed in this study is innovative because it allows teachers to provide feedback to students while monitoring their students in real time. This study will provide the basis for applying the Internet of Things to the teaching and learning fields. Specifically, the measurement model for representing the student engagement level by using thermal infrared imaging is presented. The color of the teacher's mobile phone application changes (like the traffic lights) in real time according to the immersion levels of the students in class.

关键词： Engagement Immersion in class intelligent algorithms Internet of things Thermal infrared image

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A novel approach for dynamic flow simulation of gas pipelines using teaching-learning-based optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2019年第9期233卷 3085-3099页

作者： Pourfard, Ali Khanmirza, Esmaeel Madoliat, Reza Iran Univ Sci & Technol Dept Mech Engn POB 16765-163 Tehran Iran

Simulation of a natural gas network operation is a prerequisite for optimization and control tasks. Treating gas in a transient manner is necessary for accurate simulation of gas networks. However, solving the governing nonlinear partial differential equations of pipe flows is a challenging task. In this paper, a novel approach is proposed based on using an intelligent algorithm called teaching-learning-based optimization. This approach simplifies transient simulation of gas networks with a specified type of boundary conditions. Teaching-learning-based optimization estimates different values for network inlet flow rates. Then by knowing the inlet boundary conditions of the network, the discretized flow equations become linear and the flow equations of each pipe can be solved independently. Thus, the network outlet flow variables can be easily obtained. The differences of obtained and actual network outlet flow rates are considered as a cost function or error. Finally, this intelligent algorithm determines the optimum inlet flow rates at each time level, which minimize the error. The proposed approach is implemented on the in-service gas network. To validate the simulation results, a conventional gradient-based method called trust region dogleg is also used for simulation of the gas network. The comparison of numerical results confirms the accuracy and efficiency of this approach, while it is more computationally efficient. Moreover, the substitution of teaching-learning-based optimization with another powerful intelligent optimization algorithm would not improve the performance of the proposed approach.

关键词： Transient flow gas pipelines mathematical model intelligent algorithms teaching-learning-based optimization

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A hybrid genetic artificial neural network (G-ANN) algorithm for optimization of energy component in a wireless mesh network toward green computing

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SOFT COMPUTING 2019年第8期23卷 2789-2798页

作者： Prakash, B. Jayashri, S. Karthik, T. S. Adhiparasakthi Engn Coll Dept Comp Sci & Engn Melmaruvathur Tamil Nadu India Adhiparasakthi Engn Coll Dept Elect & Commun Engn Melmaruvathur Tamil Nadu India Sapthagiri Coll Engn Dept Elect & Commun Engn Dharmapuri Tamil Nadu India

Wireless mesh networks are a special class of wireless networks that are implemented as a collection of radio nodes in a mesh pattern or topology. Unlike MANETs, the mobility of nodes is very less in the topology. Quality of service is an essential metric in the performance of mesh networks which are attributed to several parameters including optimal routing through shortest path, ability for other nodes to communicate even if a particular node in the mesh fails, minimization of packet loss and time delay, computational complexity and cost, energy. This research paper is focused toward minimization of energy taken as the objective function and a five-stage neural network is used and trained after optimizing with a genetic algorithm. The experiments have been conducted in NS2 and Qualnet environment with a varying number of mesh routers and energy computed. The performance of energy savings has been compared against conventional routing techniques like AODV and a bee colony optimization technique presented in the literature. An energy savings of 51% have been reported in the paper justifying the superiority of the hybrid G-ANN algorithm.

关键词： Wireless mesh networks Quality of service Genetic optimization intelligent algorithms Neural networks Activation function Energy savings

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An integrated framework for optimizing sculptured surface CNC tool paths based on direct software object evaluation and viral intelligence

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JOURNAL OF intelligent MANUFACTURING 2019年第4期30卷 1581-1599页

作者： Fountas, N. A. Benhadj-Djilali, R. Stergiou, C. I. Vaxevanidis, N. M. Sch Pedag & Technol Educ ASPETE Lab Mfg Proc & Machine Tools LMProMaT Dept Mech Engn Educators ASPETE Campus Athens 14121 Greece Kingston Univ Fac Sci Engn & Comp SEC Roehampton Vale CampusFriars Ave London SW15 3DW England Piraeus Univ Appl Sci Dept Mech Engn Egaleo 12244 Greece

Two critical objectives in sculptured surface tool path optimization are machining accuracy and process efficiency. The former objective is characterized as the combined effect of chord error and scallop height, known as surface machining error, whilst the latter may be reflected by the number of cutting tool locations constituting the tool path. These objectives are entirely depended on the values to be selected for computing tool paths under a given cutting strategy and preset tolerance. In order to determine optimal tool path parameters that will simultaneously satisfy the trade-off incurred between these objectives, a novel;generic and unbiased environment integrated with a virus-evolutionary heuristic for intelligent tool path optimization is presented. The proposed environment has been developed using the open architecture of a cutting edge CAM system whilst it deploys a set of interactive functions to straightforwardly assess criteria without the formal knowledge of any objective function;but directly from computer-aided manufacturing attributes;fully responsible to formulate efficient tool paths. A utility based on weighted summation for multi-objective optimization has been introduced to capture the direct output of globally optimized tool paths avoiding this way problem oversimplification and statistical errors that mathematical relations involve. Results have been rigorously validated both computationally and experimentally with the aid of a benchmark sculptured part that has been previously tested by several noticeable research contributions. Based on the quality of research outputs it is shown that the proposed framework for optimizing sculptured surface CNC tool paths may gain a prominent role for further extending the capabilities of current industrial strategies and be the flagship of allied;not-yet industrially interfaced approaches for deploying similar software integration tools to transfer significant results to production.

关键词： Sculptured surface machining Tool path optimization Viral intelligence intelligent algorithms

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Review on Application of Artificial Intelligence in Civil Engineering

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Computer Modeling in Engineering & Sciences 2019年第12期121卷 845-875页

作者： Youqin Huang Jiayong Li Jiyang Fu Joint Research Center for Engineering Structure Disaster Prevention and Control Guangzhou UniversityGuangzhou510006China

In last few years,big data and deep learning technologies have been successfully applied in various fields of civil engineering with the great progress of machine learning ***,until now,there has been no comprehensive review on its applications in civil *** fill this gap,this paper reviews the application and development of artificial intelligence in civil engineering in recent years,including intelligent algorithms,big data and deep *** the work of this paper,the research direction and difficulties of artificial intelligence in civil engineering for the past few years can be *** is shown that the studies of artificial intelligence in civil engineering mainly focus on structural maintenance and management,and the design optimization.

关键词： Artificial intelligence civil engineering intelligent algorithms big data deep learning structural maintenance

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