Study of the Electrical Characteristics, Shock-Wave Pressure Characteristics, and Attenuation Law Based on Pulse Discharge in Water

Strong shock waves can be generated by pulse discharge in water. Study of the pressure characteristics and attenuation law of these waves is highly significant to industrial production and national defense construction. In this research, the shock-wave pressures at several sites were measured by experiment under different conditions of hydrostatic pressure, discharge energy, and propagation distance. Moreover, the shock-wave pressure characteristics were analyzed by combining them with the discharge characteristics in water. An attenuation equation for a shock wave as a function of discharge energy, hydrostatic pressure, and propagation distance was fitted. The experimental results indicated that (1) an increase in hydrostatic pressure had an inhibiting effect on discharge breakdown; (2) the shock-wave peak pressure increased with increasing discharge voltage at 0.5 m from the electrode; it increased rapidly at first and then decreased slowly with increasing hydrostatic pressure; and (3) shock-wave attenuation slowed down with increasing breakdown energy and hydrostatic pressure during shock-wave transfer. These experimental results were discussed based on the mechanism described

Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers’ route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity

Turnout Fault Diagnosis through Dynamic Time Warping and Signal Normalization

Turnout is one key fundamental infrastructure in the railway signal system, which has great influence on the safety of railway systems. Currently, turnout fault diagnoses are conducted manually in China; engineers are obliged to observe the signals and make problem solving decisions. Thus, the accuracies of fault diagnoses totally depend on the engineers’ experience although massive data are produced in real time by the turnout microcomputer-based monitoring systems. This paper aims to develop an intelligent diagnosis method for railway turnout through Dynamic Time Warping (DTW). We firstly extract the features of normal turnout operation current curve and normalize the collected turnout current curves. Then, five typical fault reference curves are ascertained through the microcomputer-based monitoring system, and DTW is used to identify the turnout current curve fault through test data. The analysis results based on the similarity data indicate that the analyzed five turnout fault types can be diagnosed automatically with 100% accuracy. Finally, the benefits of the proposed method and future research directions were discussed

Study of the Electrical Characteristics, Shock-Wave Pressure Characteristics, and Attenuation Law Based on Pulse Discharge in Water

Strong shock waves can be generated by pulse discharge in water. Study of the pressure characteristics and attenuation law of these waves is highly significant to industrial production and national defense construction. In this research, the shock-wave pressures at several sites were measured by experiment under different conditions of hydrostatic pressure, discharge energy, and propagation distance. Moreover, the shock-wave pressure characteristics were analyzed by combining them with the discharge characteristics in water. An attenuation equation for a shock wave as a function of discharge energy, hydrostatic pressure, and propagation distance was fitted. The experimental results indicated that (1) an increase in hydrostatic pressure had an inhibiting effect on discharge breakdown; (2) the shock-wave peak pressure increased with increasing discharge voltage at 0.5 m from the electrode; it increased rapidly at first and then decreased slowly with increasing hydrostatic pressure; and (3) shock-wave attenuation slowed down with increasing breakdown energy and hydrostatic pressure during shock-wave transfer. These experimental results were discussed based on the mechanism described

Study of the Electrical Characteristics, Shock-Wave Pressure Characteristics, and Attenuation Law Based on Pulse Discharge in Water

Strong shock waves can be generated by pulse discharge in water. Study of the pressure characteristics and attenuation law of these waves is highly significant to industrial production and national defense construction. In this research, the shock-wave pressures at several sites were measured by experiment under different conditions of hydrostatic pressure, discharge energy, and propagation distance. Moreover, the shock-wave pressure characteristics were analyzed by combining them with the discharge characteristics in water. An attenuation equation for a shock wave as a function of discharge energy, hydrostatic pressure, and propagation distance was fitted. The experimental results indicated that (1) an increase in hydrostatic pressure had an inhibiting effect on discharge breakdown; (2) the shock-wave peak pressure increased with increasing discharge voltage at 0.5 m from the electrode; it increased rapidly at first and then decreased slowly with increasing hydrostatic pressure; and (3) shock-wave attenuation slowed down with increasing breakdown energy and hydrostatic pressure during shock-wave transfer. These experimental results were discussed based on the mechanism described

Modeling the Trend of Credit Card Usage Behavior for Different Age Groups Based on Singular Spectrum Analysis

Credit card holders from different age groups have different usage behaviors, so deeply investigating the credit card usage condition and properly modeling the usage trend of all customers in different age groups from time series data is meaningful for financial institutions as well as banks. Until now, related research in trend analysis of credit card usage has mostly been focused on specific group of people, such as the behavioral tendencies of the elderly or college students, or certain behaviors, such as the increasing number of cards owned and the rise in personal card debt or bankruptcy, in which the only analysis methods employed are simply enumerating or classifying raw data; thus, there is a lack of support in specific mathematical models based on usage behavioral time series data. Considering that few systematic modeling methods have been introduced, in this paper, a novel usage trend analysis method for credit card holders in different age groups based on singular spectrum analysis (SSA) has been proposed, using the time series data from the Survey of Consumer Payment Choice (SCPC). The decomposition and reconstruction process in the method is proposed. The results show that the credit card usage frequency falls down from the age of 26 to the lowest point at around the age of 58 and then begins to increase again. At last, future work is discussed

An EMD–SARIMA-Based Modeling Approach for Air Traffic Forecasting

The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is essential for the operations of those entities. In consideration of such a problem, a hybrid air traffic forecasting model based on empirical mode decomposition (EMD) and seasonal auto regressive integrated moving average (SARIMA) has been proposed in this paper. The model proposed decomposes the original time series into components at first, and models each component with the SARIMA forecasting model, then integrates all the models together to form the final combined forecast result. By using the monthly air cargo and passenger flow data from the years 2006 to 2014 available at the official website of the Civil Aviation Administration of China (CAAC), the effectiveness in forecasting of the model proposed has been demonstrated, and by a horizontal performance comparison between several other widely used forecasting models, the advantage of the proposed model has also been proved

Time-of-day Control Double-Order Optimization of Traffic Safety and Data-Driven Intersections

This paper proposes a novel two-order optimization model of the division of time-of-day control segmented points of road intersection to address the limitations of the randomness of artificial experience, avoid the complex multi-factor division calculation, and optimize the traditional model over traffic safety and data-driven methods. For the first-order optimization—that is, deep optimization of the model input data—we first increase the dimension of traditional traffic flow data by data-driven and traffic safety methods, and develop a vector quantity to represent the size, direction, and time frequency with conflict point traffic of the total traffic flow at a certain intersection for a period by introducing a 3D vector of intersection traffic flow. Then, a time-series segmentation algorithm is used to recurse the distance amongst adjacent vectors to obtain the initial scheme of segmented points, and the segmentation points are finally divided by the combination of the preliminary scheme. For the second-order optimization—that is, model adaptability analysis—the traffic flow data at intersections are subjected to standardised processing by five-number summary. The different traffic flow characteristics of the intersection are categorised by the K central point clustering algorithm of big data, and an applicability analysis of each type of intersection is conducted by using an innovated piecewise point division model. The actual traffic flow data of 155 intersections in Yuecheng District, Shaoxing, China, in 2016 are tested. Four types of intersections in the tested range are evaluated separately by the innovated piecewise point division model and the traditional total flow segmentation model on the basis of Synchro 7 simulation software. It is shown that when the innovated double-order optimization model is used in the intersection according to the ‘hump-type’ traffic flow characteristic, its control is more accurate and efficient than that of the traditional total flow segmentation model. The total delay time is reduced by approximately 5.6%. In particular, the delay time in the near-peak-flow buffer period is significantly reduced by approximately 17%. At the same time, the traffic accident rate has also dropped significantly, effectively improving traffic safety at intersections

Predicting Pedestrian Counts for Crossing Scenario Based on Fused Infrared-Visual Videos

Estimating the number of pedestrians based upon surveillance videos and images has been a critical task while implementing intelligent signal controls at intersections. However, this has been a difficult task considering the pedestrian waiting area is an outdoor scenario with complex and time-varying surrounding environment. In this study, a method for estimating pedestrian counts based on multisource video data has been proposed. First, the partial least squares regression (PLSR) model is developed to estimate the number of pedestrians from single-source video (either visible light video or infrared video). Meanwhile, the temporal feature of the scenario (daytime or nighttime) is identified based on visible light video. According to the recognized time periods, pedestrian count detection results from the visible light and infrared video data can be obtained with preset corresponding confidence levels. The empirical experiments showed that this fusion method based on environment perception holds the benefits of 24-hour monitoring for outdoor scenarios at the pedestrian waiting area and substantially improved accuracy of pedestrian counting