Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with small data is troublesome. Extensive data is necessary for training machine learning algorithms for effective prediction. Data generation is a method for expanding the amount of data that will be further accessible for training. In this work, we proposed a model that takes time-series customers’ electric kickboard demand data as input, pre-processes it, and generates synthetic data according to the original data distribution using generative adversarial networks (GAN). The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data. We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results. We modified The Wasserstein GAN-gradient penalty (GP) with the RMSprop optimizer and then employed Spectral Normalization (SN) to improve training stability and faster convergence. Finally, we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction. We used various evaluation criteria and visual representations to compare our proposed model’s performance. Synthetic data generated by our suggested GAN model is also evaluated. The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem, and it also converges faster than previous GAN models for synthetic data creation. The presented model’s performance is compared to existing ensemble and baseline models. The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error (MAPE) of 4.476 and increase prediction accuracy.     

The Role of Deep Learning in Parking Space Identification andPredictionSystems

In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.     

Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

Data mining process involves a number of steps from data collection to visualization to identify useful data from massive data set. the same time, the recent advances of machine learning (ML) and deep learning (DL) models can be utilized for effectual rainfall prediction. With this motivation, this article develops a novel comprehensive oppositional moth flame optimization with deep learning for rainfall prediction (COMFO-DLRP) Technique. The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes. Primarily, data pre-processing and correlation matrix (CM) based feature selection processes are carried out. In addition, deep belief network (DBN) model is applied for the effective prediction of rainfall data. Moreover, COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning (COBL) with traditional MFO algorithm. Finally, the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN model. For demonstrating the improved outcomes of the COMFO-DLRP approach, a sequence of simulations were carried out and the outcomes are assessed under distinct measures. The simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.     

无人驾驶车多种空间人类移动性交互设计研究

目的探讨无人驾驶车在智能交通系统、大数据、人工智能等技术支持下,对人类移动性的范式拓展与设计思考,研究新型人类移动性下无人驾驶车的交互设计方法。方法采用文献调查法、田野调查法和用户参与式设计方法进行分析研究。结果无人驾驶车对人类移动性的拓展包含信息转化,时空维度转换,固有信息与即生信息、转化信息构成了人类新的社交网络以及移动性的内容知识图谱。链接物资流、信息流和资金流的人类移动性构建在多种空间的灵活信息架构中。结论新型人类移动性的无人驾驶车交互设计,将交互内容、功能、媒介的设计,与人类移动性的数据采集、数据处理、数据应用相映射,交互模型中人的身份变得多元,人类群体移动性的交互模式形成新的交互行为逻辑,交互环境由人工智能数字环境和人类智能非数字环境共同构成混合智能的交互样态,信息架构基于自动驾驶形成自适应用户自产生内容UGC前馈和无人驾驶车专业生产内容PGC反馈的智能信息,新的人类移动性催生出由交互载体、功能、内容三要素构成的新的人机交互范式。     

用ANN法对房间空调冷负荷进行预测的讨论

通过影响空调负荷的参数的研究,认为空调负荷是一个动态过程;结合神经网络的内在特点和功能,对某一空调系统的冷负荷进行了预测,结果能满足计算要求.在这基础上考虑了为提高神经网络预测空调负荷准确性还应进一步开展的工作.     

Stock Price Prediction Using Predictive Error Compensation Wavelet Neural Networks

Machine Learning (ML) algorithms have been widely used for financial time series prediction and trading through bots. In this work, we propose a Predictive Error Compensated Wavelet Neural Network (PEC-WNN) ML model that improves the prediction of next day closing prices. In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs. An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence. The performance of the proposed model is evaluated using six different stock data samples in the New York stock exchange. The results have demonstrated significant improvement in forecasting accuracy in all cases when the second network is used in accordance with the first one by adding the outputs. The RMSE error is 33% improved when the proposed PEC-WNN model is used compared to the Long Short-Term Memory (LSTM) model. Furthermore, through the analysis of training mechanisms, we found that using the updated training the performance of the proposed model is improved. The contribution of this study is the applicability of simultaneously different time frames as inputs. Cascading the predictive error compensation not only reduces the error rate but also helps in avoiding overfitting problems.     

Neural Network Burst Pressure Prediction in Composite Overwrapped Pressure Vessels Using Mathematically Modeled Acoustic Emission Failure Mechanism Data

The purpose of this research was to predict burst pressures in composite overwrapped pressure vessels (COPVs) by using mathematically modeled acoustic emission (AE) data. Both backpropagation neural network (BPNN) and multiple linear regression (MLR) analyses were performed on various subsets of the low proof pressure AE data to predict burst pressures and to determine if the two methods were comparable. AE data were collected during hydrostatic burst testing on the 15-inch diameter COPVs. Once collected, the AE data were filtered to eliminate noise then classified into AE failure mechanism data using a MATLAB Kohonen self-organizing map (SOM). The matrix cracking only amplitude distribution data were mathematically modeled using bounded Johnson distributions with the four Johnson distribution parameters – ?, λ, γ, and η – employed as inputs to make both the BPNN and MLR predictions. The burst pressure predictions generated using a MATLAB BPNN resulted in a worst case error of 1.997% as compared to ?1.666% for the MLR analysis, suggesting comparability. However, the MLR analysis required the data from all nine COPVs to get approximately the same results as the BPNN training on just five COPVs; plus, MLR analyses are intolerant to noise, whereas BPNNs are not.     

Human diffusion and city influence

Cities are characterized by concentrating population, economic activity and services. However, not all cities are equal and a natural hierarchy at local, regional or global scales spontaneously emerges. In this work, we introduce a method to quantify city influence using geolocated tweets to characterize human mobility. Rome and Paris appear consistently as the cities attracting most diverse visitors. The ratio between locals and non-local visitors turns out to be fundamental for a city to truly be global. Focusing only on urban residents'' mobility flows, a city-to-city network can be constructed. This network allows us to analyse centrality measures at different scales. New York and London play a central role on the global scale, while urban rankings suffer substantial changes if the focus is set at a regional level.     

Condition‐based Maintenance Optimization Using Neural Network‐based Health Condition Prediction

Artificial neural network (ANN)‐based methods have been extensively investigated for equipment health condition prediction. However, effective condition‐based maintenance (CBM) optimization methods utilizing ANN prediction information are currently not available due to two key challenges: (i) ANN prediction models typically only give a single remaining life prediction value, and it is hard to quantify the uncertainty associated with the predicted value; (ii) simulation methods are generally used for evaluating the cost of the CBM policies, while more accurate and efficient numerical methods are not available, which is critical for performing CBM optimization. In this paper, we propose a CBM optimization approach based on ANN remaining life prediction information, in which the above‐mentioned key challenges are addressed. The CBM policy is defined by a failure probability threshold value. The remaining life prediction uncertainty is estimated based on ANN lifetime prediction errors on the test set during the ANN training and testing processes. A numerical method is developed to evaluate the cost of the proposed CBM policy more accurately and efficiently. Optimization can be performed to find the optimal failure probability threshold value corresponding to the lowest maintenance cost. The effectiveness of the proposed CBM approach is demonstrated using two simulated degradation data sets and a real‐world condition monitoring data set collected from pump bearings. The proposed approach is also compared with benchmark maintenance policies and is found to outperform the benchmark policies. The proposed CBM approach can also be adapted to utilize information obtained using other prognostics methods. Copyright © 2012 John Wiley & Sons, Ltd.     

A Novel Method of Heart Failure Prediction Based on DPCNNXGBOOST Model

The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients. Existing methods depend on the judgment of doctors, the results are affected by many factors such as doctors’ knowledge and experience. The accuracy is difficult to guarantee and has a serious lag. In this paper, a mixture prediction model is proposed for perioperative adverse events of heart failure, which combined with the advantages of the Deep Pyramid Convolutional Neural Networks (DPCNN) and Extreme Gradient Boosting (XGBOOST). The DPCNN was used to automatically extract features from patient’s diagnostic texts, and the text features were integrated with the preoperative examination and intraoperative monitoring values of patients, then the XGBOOST algorithm was used to construct the prediction model of heart failure. An experimental comparison was conducted on the model based on the data of patients with heart failure in southwest hospital from 2014 to 2018. The results showed that the DPCNN-XGBOOST model improved the predictive sensitivity of the model by 3% and 31% compared with the text-based DPCNN Model and the numeric-based XGBOOST Model.     

具分布时滞的周期运动细胞神经网络周期解的存在性(英文)

运用Mawhin连续性定理研究具分布时滞的周期运动细胞神经网络周期解的存在性,假设行为函数位于一带型区域内,激活函数位于两线性函数所夹的区域内。     

Research on PM_2.5 Concentration Prediction Algorithm Based on Temporal and Spatial Features

PM_2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate, and it is an evaluation indicator of air pollution level. Achieving PM_2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and control. The past methods neglected the impact caused by PM_2.5 flow between cities when analyzing the impact of inter-city PM_2.5 concentrations, making it difficult to further improve the prediction accuracy. However, factors including geographical information such as altitude and distance and meteorological information such as wind speed and wind direction affect the flow of PM_2.5 between cities, leading to the change of PM_2.5 concentration in cities. So a PM_2.5 directed flow graph is constructed in this paper. Geographic and meteorological data is introduced into the graph structure to simulate the spatial PM_2.5 flow transmission relationship between cities. The introduction of meteorological factors like wind direction depicts the unequal flow relationship of PM_2.5 between cities. Based on this, a PM_2.5 concentration prediction method integrating spatial-temporal factors is proposed in this paper. A spatial feature extraction method based on weight aggregation graph attention network (WGAT) is proposed to extract the spatial correlation features of PM_2.5 in the flow graph, and a multi-step PM_2.5 prediction method based on attention gate control loop unit (AGRU) is proposed. The PM_2.5 concentration prediction model WGAT-AGRU with fused spatiotemporal features is constructed by combining the two methods to achieve multi-step PM_2.5 concentration prediction. Finally, accuracy and validity experiments are conducted on the KnowAir dataset, and the results show that the WGAT-AGRU model proposed in the paper has good performance in terms of prediction accuracy and validates the effectiveness of the model.     

Ultimate Strength Prediction of Carbon/Epoxy Tensile Specimens from Acoustic Emission Data

The objective of this paper was to predict the residual strength of post impacted carbon/epoxy composite laminates using an online acoustic emission (AE) monitoring and artificial neural networks (ANN). The laminates were made from eight-layered carbon (in woven mat form) with epoxy as the binding medium by hand lay-up technique and cured at a pressure of 100 kg/cm2 under room temperature using a 30 ton capacity compression molding machine for 24 h. 21 tensile specimens (ASTM D3039 standard) were cut from t...     

矩形盒拉深变压边力加载规律及预测

目的解决板料拉深过程中出现拉裂、起皱、拉深不充分等缺陷的问题。方法利用专业分析板料成形的软件Dynaform,研究分析了非轴对称件矩形盒,在几种典型的变压边力下的拉深成形性能,获得了成形效果较好的加载模式,进而利用仿真软件Dynaform获取了样本数据。结果建立了矩形盒拉深成形变压边力网络模型并对其学习训练,最后对神经网络预测结果及仿真结果所得到的变压边力加载曲线进行多项式拟合,获取了最佳压边力控制曲线。结论在板料拉深过程中,通过控制压边力的大小,能够较好地发挥材料的流动性,改善制件的最终成形效果。     

Research on rehabilitation training bed with action prediction based on NARX neural network

Rehabilitation training can effectively help the elderly recover their self-care state and enhance physical fitness. As surface electromyography analysis is effective to recognize motion intention, researchers use it to develop prosthetic limb operations. In this article, the rehabilitation training bed is designed by combining the rehabilitation training with the motion prediction based on the surface myoelectric signal, which can recognize the tilt of the upper body in different directions and provide corresponding assistance to the elderly. After collecting EMG signal, the effective signal was dimensional reduction, mapped by linear discriminant analysis. To train and recognize the EMG-motion mapping relationship, we used a recurrent neural network called nonlinear autoregressive with exogenous input model and used a 360° tilt prediction experiment on the upper body. Results showed that the root mean squared error and the error autocorrelation coefficient were relatively low, and the tilt degree of the experimenter was highly matched.     

Theory and data for simulating fine-scale human movement in an urban environment

Individual-based models of infectious disease transmission depend on accurate quantification of fine-scale patterns of human movement. Existing models of movement either pertain to overly coarse scales, simulate some aspects of movement but not others, or were designed specifically for populations in developed countries. Here, we propose a generalizable framework for simulating the locations that an individual visits, time allocation across those locations, and population-level variation therein. As a case study, we fit alternative models for each of five aspects of movement (number, distance from home and types of locations visited; frequency and duration of visits) to interview data from 157 residents of the city of Iquitos, Peru. Comparison of alternative models showed that location type and distance from home were significant determinants of the locations that individuals visited and how much time they spent there. We also found that for most locations, residents of two neighbourhoods displayed indistinguishable preferences for visiting locations at various distances, despite differing distributions of locations around those neighbourhoods. Finally, simulated patterns of time allocation matched the interview data in a number of ways, suggesting that our framework constitutes a sound basis for simulating fine-scale movement and for investigating factors that influence it.     

Optimization of Cutting Parameters in Face Milling with Neural Networks and Taguchi based on Cutting Force,Surface Roughness and Temperatures

Prediction of cutting parameters as a function of cutting force, surface roughness and cutting temperature is very important in face milling operations. In the present study, the effect of cutting parameters on the mentioned responses were investigated by using artificial neural networks (ANN) which were trained by using experimental results obtained from Taguchi’s L8 orthogonal design. The experimental results are compared with the results predicted by ANN and the Taguchi method. By training the ANN with the results of experiments which are corresponding with the Taguchi L8 design, with only eight experiments an effective ANN model is trained. By using this network model the other combinations of experiments which did not perform previously, could be predicted with acceptable error.     

Mechanical Property Prediction of Commercially Pure Titanium Welds with Artificial Neural Network

Factors that affect weld mechanical properties of commercially pure titanium have been investigated using artificial neural networks. Input data were obtained from mechanical testing of single-pass, autogenous welds, and neural network models were used to predict the ultimate tensile strength, yield strength, elongation, reduction of area, Vickers hardness and Rockwell B hardness. The results show that both oxygen and nitrogen have the most significant effects on the strength while hydrogen has the least effect over the range investigated. Predictions of the mechanical properties are shown and agree well with those obtained using the 'oxygen equivalent' (OE) equations.