Port Container Throughput Forecast Based on ABC Optimized BP Neural Network

As a nonlinear variation, the port container throughput is easy to get affected by market. There are many prediction methods such as regression analysis, exponential smoothing. However, each of these prediction methods has their own characters which all lead to low precision of container throughput forecasting. Constructed on the foundation of BP network, the combination forecast model performs well in time series forecasting and solves the problem excellently. Consequently, based on Elman neural network, this article builds a combination forecast model to improve the precision level of forecast. At last, with the empirical analysis of container throughput in Shanghai port, the reliability and accuracy of the combination model are proved.

Accurate forecast of container throughput is an important basis for reasonably planning port construction, making port operation plan and adjusting port development direction. Aiming at the complex nonlinear characteristics of port container throughput, a hybrid model based on selective deep-ensemble for container throughput forecasting (HMSD) is presented in this paper. Firstly, this model decomposes the original container throughput time series into several intrinsic mode functions and a residual by empirical mode decomposition (EMD). Considering highly nonlinear characteristics of each intrinsic mode function, the proposed model constructs three deep neural networks, namely, long short term memory (LSTM), gated recurrent unit (GRU) and convolutional neural network (CNN), as base learners to predict intrinsic mode functions. Then, this model establishes selective deep-ensemble forecasting model by improved group method of data handling (GMDH) on intrinsic mode functions and obtains their ensemble forecasting results. Furthermore, this model uses an autoregressive integrated moving average model to predict the linear residual. Finally, the forecasting result of the total container throughput is obtained by integrating the forecasting results of all intrinsic mode functions and the residual. The empirical analysis of container throughput data of Xiamen port and Shanghai port in China shows that the HMSD model has better forecasting effect than other models.

Seasonal container throughput forecasts at ports are immensely important to logistics companies, shipping lines, port authorities and shipyards. Such forecasts allow shipping lines and port operators to formulate appropriate short-to-medium strategies in order to maintain competitiveness. Seasonal autoregressive integrated moving average (in short, SARIMA) models can be employed for this purpose to provide reliable seasonal forecasts of container throughput at a given container port. This article explores the use of SARIMA models in forecasting container throughput at several major international container ports, while taking into consideration seasonal variations. First, the SARIMA model development methodology is described. Second, a database consisting of monthly container port traffic data between 1999 and 2007 for international container ports is developed. Short-term container demand forecasting models are then developed for each of the top 20 international container ports for the purpose of monthly container throughput prediction. Through the use of various performance metrics, the effectiveness of the developed SARIMA models for these ports is evaluated. It is found that SARIMA models can produce reliable throughput forecasts at major international ports. Qualitative insights are then drawn, thereby allowing shipping and port operators to make better tactical and operational decisions.

Real-time detection and closed-loop control of laser cladding height is necessary for forming high quality parts. Technological parameters are coupled and the forming process is a non-linear process. A large number of laser parameters affect the quality of the laser cladding surface. Based on the analysis of the influence of laser parameters on cladding height, the BP (Back propagation) neural network prediction model of cladding height is build. The neural network arithmetic is designed and the samples are acquired by laser forming experiment. The training samples are used to train the network to accomplish the mapping relation between input and output of the network. The test samples are used to verify the performance of the trained network. Simulation results indicate that the prediction model has sufficient accuracy. The BP neural network prediction model of cladding height is feasible and valid in theory and in practice. The laser cladding height BP neural network prediction model lays the foundation for real-time height prediction and closed-loop control in laser forming process, and it has great significance for improving the quality of formed parts.

Forecasting the demand for container throughput is a critical indicator to measure the development level of a port in global business management and industrial development. Time-series analysis approaches are crucial techniques for forecasting the demand for container throughput. However, accurate demand forecasting for container throughput remains a challenge in time-series analysis approaches. In this study, we proposed a mixed-precision neural architecture to forecasting the demand for container throughput. This study is the first work to use a mixed-precision neural network to forecast the container throughput—the mixed-precision architecture used the convolutional neural network for learning the strength of the features and used long short-term memory to identify the crucial internal representation of time series depending on the strength of the features. The experiments on the demand for container throughput of the five ports in Taiwan were conducted to compare our deep learning architecture with other forecasting approaches. The results indicated that our mixed-precision neural architecture exhibited higher forecasting performance than classic machine learning approaches, including adaptive boosting, random forest regression, and support vector regression. The proposed architecture can effectively predict the demand for port container throughput and effectively reduce the costs of planning and development of ports in the future.

Poly(methyl methacrylate) (PMMA) is widely used in the preservation and exhibition of cultural relics in museums. Accurately predicting its service life can help avoid many negative effects caused by PMMA aging. To study the change in the yellowing index of PMMA after aging in a UV light environment, an aging experiment was conducted. A prediction model for the service life of PMMA was established using nonlinear curve fitting and a back propagation (BP) neural network. By comparing the goodness of fit, simulation and modeling capabilities of the initial data, and the predictive ability for new data, it was found that the BP neural network prediction model outperformed the nonlinear curve fitting prediction model. In this study, the service life of newly produced PMMA samples was calculated as 7.83, 8.47, and 8.42 years, based on the yellowing index of retired PMMA as a benchmark and using the output data from the BP neural network prediction model. At this time, the performance and exhibition effect of the PMMA are poor, and the batch of PMMA needs to be updated.

The control of matte grade determines the production cost of the copper smelting process. In this paper, an optimal matte-grade control model is established to derive the optimal matte grade with the objective of minimizing the cost in the whole process of copper smelting. This paper also uses the prediction capability of the BP (Backpropagation) neural network to establish a BP neural network prediction model for the matte grade, considering various factors affecting matte grade (including the input copper concentrate amount and its composition content, air drumming amount, oxygen drumming amount, melting agent amount, and other process parameters). In addition, the paper also uses the optimal matte grade to optimize the dosing, air supply/oxygen supply, and oxygen supply for the ISA and other furnaces. When using BP networks only, it is a nonconvex problem with gradient descent, which tends to fall into local minima and has some bias in the prediction results. This problem can be solved by optimizing its weights and thresholds through GA (Genetic Algorithm) to find the optimal solution. The analysis results show that the average absolute error of the simulation of the BP neural network prediction model for ice copper grade after GA optimization is 0.51%, which is better than the average absolute error of 1.17% of the simulation of the single BP neural network model.

Addressing the difficult problem of predicting the height of water-conducting fracture zones in shallow and thin coal seams, a prediction model of water-conduction fracture zones based on a backpropagation (BP) neural network was developed by integrating theoretical analysis, field measurements, and algorithmic advancements. Firstly, through overburden migration analysis and correlation tests, the height index system of the water-conducting fracture zone was determined. This system includes mining height, buried depth, dip angle, working face width, and overburden rock lithology, with five groups of characteristic parameters. Then, 35 pairs of minefield-measured data were collected to establish the measured height data set of the water-conducting fracture zone. Secondly, a BP neural network prediction model and a traditional support vector regression (SVR) prediction model were constructed based on a Pytorch framework, and the models were trained and tested by selecting data sets. Thirdly, the optimal prediction model was determined by comparing the model with the empirical model and multiple regression model of mining regulations for coal pillar maintenance and pressure in buildings, water bodies, railways, and main shafts. Finally, a typical mine was selected for application to verify the suitability of the optimal model. The results show that: (1) the predicted value of the neural network model is consistent with the change trend of the measured value, which accords with the theoretical law; (2) compared with traditional forecasting methods, the error of the BP neural network prediction model is stable and the prediction effect is the best; (3) dropout can effectively mitigate mitigation training overfitting, achieve regularization, and improve prediction accuracy; (4) the field application further verified that the BP neural network model is the best for predicting the height of water-conducting fracture zones of extremely thin coal seams, and the research results can provide technical guidance for similar fragile coal seams.

The statistical techniques based on compositional data are applied to investigate the evolution of the traffic share of the container throughput in a multi-port system. Compositional vectors are those which contain relative information of parts of some whole. The application of conventional statistical techniques to compositional data may lead to erroneous conclusions and spurious correlations. Therefore, compositional data (CoDa) should be treated taking into account their own mathematical structure. The so-called log-ratio approach provides a set of transformations that allow to apply conventional statistical techniques to the transformed compositional data samples. Thus, the objective of this paper is double. As a first stage it aims to introduce the CoDa formalism and highlight its potentiality in the port container throughput analysis as example of transport system providing an applied example: the container throughput evolution in the Spanish Mediterranean Ports system during the period 1976–2015. Second, based on the previous analysis, the aim is to characterize the container throughput in SpanishMed ports and its temporal evolution. The CoDa analysis clarifies the interpretation and data association of the container traffic throughput evolution in function of some selected change points: boom of containerization in 1990s and 2008 crisis. This contribution proves that the CoDa methodology is useful to investigate the complexity of the transport disciplines in order to understand and to manage the spatial integration that results from the movement of people and freight.

Software aging is a common phenomenon that exists in systems that require long periods of operation, especially in Internet-of-Things environments. The back propagation (BP) neural network has been adopted widely to predict the trend of software aging. However, the weight and threshold of the BP neural network are randomly initialized, so it is easy to get the unsatisfactory local optimal solutions and the convergence speed of computing is slow. In this paper, we propose a novel software aging prediction method using the artificial bee colony algorithm to optimize the BP neural network model for achieving better software aging prediction accuracy. The experiment results show that our method fits the prediction trend of software aging more accurately than the traditional BP neural network, and our method also has faster convergence speed and more stable prediction results.

The Back Propagation (BP) neural network genetic algorithm was used to identify alphabet, and the new algorithm combine the advantages of both genetic algorithm and the BP neural network. Genetic learning algorithm was used for the global optimization and BP training algorithm to accurately optimize the neural network weights and training the neural network to learn letter recognition algorithm. Add-noise alphabet of MATLAB simulation results show that the new network error recognition rate reduced by 10% compared to BP neural network and the recognition speed is also faster than the traditional BP neural network with accuracy and fast convergence.

For the purpose of analyzing the application of back propagation (BP) neural network model in retinal vascular segmentation of color fundus images, in this study, the traditional BP neural network was first improved by adopting the additional momentum method. Second, adaptive histogram, morphological background subtraction, Gauss preprocessing matching filter and Heisen matrix were used to enhance the image and extract the features. Third, the retinal vascular segmentation algorithm for color fundus images was constructed based on optimized BP neural network. Finally, the DRIVE and MESSIDOR data sets of color fundus images were introduced to compare the proposed algorithm with the convolutional neural network (CNN) and pulse-coupled neural network (PCNN) algorithms in terms of performance. In addition, the three algorithms were also compared in terms of the sensitivity (Se), specificity (Sp), accuracy (Ac), average operation time for each image and F1 value. The results show that the BP neural network algorithm proposed in this study shows obvious advantage over the other two algorithms in the segmentation of color fundus images. In the DRIVE data set, the Se (81.37%), Sp (90.55%) and Ac (95.82%) of BP algorithm are the highest among the three; in the MESSIDOR data set, the Se (85.22%), Sp (91.08%) and Ac (96.16%) of BP algorithm are also highest; in the DRIVE and MESSIDOR data sets, the operation time of BP algorithm is (28.46 ± 3.19 ms; 24.73 ± 4.81 ms, respectively), which are significantly less than the other two algorithms. Besides, the F1 value of the proposed algorithm is obviously higher than that the other two algorithms. As a result, it is concluded that compared with the CNN algorithm and the PCNN algorithm, the proposed algorithm is more effective in the retinal vascular segmentation of color fundus images; the capillaries have better connectivity, and the proposed algorithm can improve the segmentation Ac while reducing the operation time.

Improving the resilience of enterprise safety production is one of the important ways to deal with the frequency of safety accidents. Based on the definition of enterprise safety production resilience, we fully consider the impacts of recovery resilience, self-organizing resilience, and learning resilience as the three dimensions of enterprise safety production resilience. We build a back propagation (BP) neural network model that analyzes the main factors of enterprise safety production resilience using the results of gray relational analysis as an input that can assess the resilience of enterprise safety production and provide a valuable reference for the improvement of an enterprise’s safety production level. The results show that the resilience of production safety obviously increased after the Chinese enterprises with low resilience (as predicted by the model) adopted the corresponding early warning methods. The gray relational degree analysis method can incorporate well the variables for the establishment of the BP neural network prediction model.

Due to the international transfer of manufacturing industry, the change of trade policy and frequent irregular events in the global trade, it becomes more difficult to predict port container throughput accurately. In order to improve the predictive accuracy, we develop a bidirectional long short-term memory network model to forecast the throughput. Using the data of port in Qingdao, this study investigates for the first time how to use the deep learning approach to predict port container throughput. The empirical results show that the proposed model can achieve highest average predictive accuracy, which indicates that the approach is effective in the increasingly complex trade situation.

The X-Press Pearl disaster has the unfortunate hallmarks of other global container ship disasters that are becoming increasingly common and will continue to cause future environmental damage. Port container throughput is ~830 million twenty-foot equivalents (TEUs), projected to grow for the next four decades at minimum, and industry experts predict that ship sizes will continue to grow to 50,000 TEU. Misdeclared content, poor packing, inadequate stowage/lashing, ship-board fires, climate-change-driven intensity of storms, and increasing ship size all ensure increasing environmental damage from container ship maritime disasters. Industry consensus is that the number of containers lost at sea is conspicuously undercounted, and the impact of lost containers is unstudied. This paper reviews specific aspects of the container ship industry that contribute to environmental damage, and then addresses needs for risk, impact assessment and environmental management as pertains to industry operations and container ship disasters such as the X-Press Pearl.