A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis

Road traffic accident databases provide the basis for road traffic accident analysis, the data inside which usually has a radial, multidimensional, and multilayered structure. Traditional data mining algorithms such as association rules, when applied alone, often yield uncertain and unreliable results. An improved association rule algorithm based on Particle Swarm Optimization (PSO) put forward by this paper can be used to analyze the correlation between accident attributes and causes. The new algorithm focuses on characteristics of the hyperstereo structure of road traffic accident data, and the association rules of accident causes can be calculated more accurately and in higher rates. A new concept of Association Entropy is also defined to help compare the importance between different accident attributes. T-test model and Delphi method were deployed to test and verify the accuracy of the improved algorithm, the result of which was a ten times faster speed for random traffic accident data sampling analyses on average. In the paper, the algorithms were tested on a sample database of more than twenty thousand items, each with 56 accident attributes. And the final result proves that the improved algorithm was accurate and stable

Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing

Lack of assurance of quality with additively manufactured (AM) parts is a key technological barrier that prevents manufacturers from adopting AM technologies, especially for high-value applications where component failure cannot be tolerated. Developments in process control have allowed significant enhancement of AM techniques and marked improvements in surface roughness and material properties, along with a reduction in inter-build variation and the occurrence of embedded material discontinuities. As a result, the exploitation of AM processes continues to accelerate. Unlike established subtractive processes, where in-process monitoring is now commonplace, factory-ready AM processes have not yet incorporated monitoring technologies that allow discontinuities to be detected in process. Researchers have investigated new forms of instrumentation and adaptive approaches which, when integrated, will allow further enhancement to the assurance that can be offered when producing AM components. The state-of-the-art with respect to inspection methodologies compatible with AM processes is explored here. Their suitability for the inspection and identification of typical material discontinuities and failure modes is discussed with the intention of identifying new avenues for research and proposing approaches to integration into future generations of AM systems

Fatigue Driving Prediction on Commercial Dangerous Goods Truck Using Location Data: The Relationship between Fatigue Driving and Driving Environment

The approaches monitoring fatigue driving are studied because of the fact that traffic accidents caused by fatigue driving often have fatal consequences. This paper proposes a new approach to predict driving fatigue using location data of commercial dangerous goods truck (CDT) and driver’s yawn data. The proposed location data are from an existing dataset of a transportation company that was collected from 166 vehicles and drivers in an actual driving environment. Six different categories of the predictor set are considered as fatigue-related indexes including travel time, day of week, road type, continuous driving time, average velocity, and overall mileage. The driver’s yawn data are used as a proxy for ground truth for the classification algorithm. From the six different categories of the predictor set, we obtain a set of 17 predictor variables to train logistic regression, neural network, and random forest classifiers. Then, we evaluate the predictive performance of the classifiers based on three indexes: accuracy, F1-measure, and area under the ROC curve (AUROC). The results show that the random forest is more suitable for predicting fatigue driving using location data according to its best accuracy (74.18%), F1-measure (62.02%), and AUROC (0.8059). Finally, we analyze the relationship between fatigue driving and driving environment according to variable importance described by random forest. In summary, our results obviously exhibit the potential of location data for reducing the accident rate caused by fatigue driving in practice

Switching Mechanism on the Order of Affine Projection Algorithm

Conventional affine projection (AP) algorithm with a fixed order is subject to a tradeoff between convergence speed and steady-state misalignment. In order to address such problem, a switching mechanism on the order of AP algorithm is proposed by comparing the performance of two AP algorithms with different orders. Firstly, the mean square deviations (MSD) behavior of the AP algorithm is analyzed, and a calculation formula for computing MSD at each iteration is derived. Secondly, we design a switching mechanism to select the better order of the two AP algorithms by comparing the MSDs of them; the MSD of the chosen order is smaller than that of the other. We also give the theoretical analysis, including steady-state mean square error (MSE) and computational complexity. Finally, the experiments in system identification and echo-cancellation scenarios demonstrate that the proposed algorithm has good performance not only in a stationary environment but also in a non-stationary environment

Acoustic echo cancellation based on two‐stage BLSTM

Abstract Acoustic echo cancellation (AEC) methods aim to suppress the acoustic coupling for hands‐free speech communication. Traditional AEC works by identifying the acoustic impulse response using adaptive algorithms. With recent research advances, deep learning has become an attractive choice for AEC. This paper introduces a two‐stage bidirectional long short term memory (TS‐BLSTM) framework, incorporating multi‐head self‐attention mechanisms after each BLSTM block. This is aimed at better capturing contextual information and further enhancing ability of the model to handle complex acoustic scenarios. The BLSTM blocks are utilized to aggregate magnitude spectrum information, modelling both time and frequency dependencies. Additionally, dilation convolution is introduced to broaden the range of information in each convolution output. The magnitude decoder estimates a mask for the input, resulting in the generation of an estimated magnitude spectrum for near‐end speech. Experimental results indicate that the proposed method achieves promising outcomes

Exploring spatial variation of the bus stop influence zone with multi-source data: A case study in Zhenjiang, China

Bus stops are important traffic facilities that affect the efficiency of transportation system as well as the characteristics of bus emissions, and the bus stop influence zone (BSIZ) is the basic to estimate the bus emissions. The primary objective of this study is to investigate how the potential factors affect the length of BSIZ. In this study, the geographically weighted regression (GWR) model was implemented to build a relationship between the length of BSIZ and various contributing factors. The spatial heterogeneity of the length of BSIZ was explored, and the spatial distributions of parameter estimations were visualized. Five types of data including bus emission data, global positioning system (GPS) data, point of interest (POI) data, bus stop feature data, and road feature data were collected from Zhenjiang in China to illustrate the procedure. The results indicated that the urban form has a significant impact on the length of BSIZ, and strong spatial variability for the length of BSIZ is observed. The number of enterprises and companies around bus stops, the distance between the stop and intersection, road hierarchy, the number of public facilities, the queue length of buses, as well as traffic volume can significantly affect the length of BSIZ, and the estimated coefficients of each bus stop vary across regions. The results provided valuable insights which contribute to quantify and estimate the emissions generated near bus stops

The New High-Pressure Phases of Nitrogen-Rich Ag–N Compounds

The high-pressure phase diagram of Ag–N compounds is enriched by proposing three stable high-pressure phases (P4/mmm-AgN2, P1-AgN7 and P-1-AgN7) and two metastable high-pressure phases (P-1-AgN4 and P-1-AgN8). The novel N7 rings and N20 rings are firstly found in the folded layer structure of P-1-AgN7. The electronic structure properties of predicted five structures are studied by the calculations of the band structure and DOS. The analyses of ELF and Bader charge show that the strong N–N covalent bond interaction and the weak Ag–N ionic bond interaction constitute the stable mechanism of Ag–N compounds. The charge transfer between the Ag and N atoms plays an important role for the structural stability. Moreover, the P-1-AgN7 and P-1-AgN8 with the high-energy density and excellent detonation properties are potential candidates for new high-energy density species

Cerium-promoted conversion of dinitrogen into high-energy-density material CeN6 under moderate pressure

Synthesis pressure and structural stability are two crucial factors for highly energetic materials, and recent investigations have indicated that cerium is an efficient catalyst for N2 reduction reactions. Here, we systematically explore Ce–N compounds through first-principles calculations, demonstrating that the cerium atom can weaken the strength of the N≡N bond and that a rich variety of cerium polynitrides can be formed under moderate pressure. Significantly, P1̄-CeN6 possesses the lowest synthesis pressure of 32 GPa among layered metal polynitrides owing to the strong ligand effect of cerium. The layered structure of P1̄-CeN6 proposed here consists of novel N14 ring. To clarify the formation mechanism of P1̄-CeN6, the reaction path Ce + 3N2 → trans-CeN6 → P1̄-CeN6 is proposed. In addition, P1̄-CeN6 possesses high hardness (20.73 GPa) and can be quenched to ambient conditions. Charge transfer between cerium atoms and N14 rings plays a crucial role in structural stability. Furthermore, the volumetric energy density (11.20 kJ/cm3) of P1̄-CeN6 is much larger than that of TNT (7.05 kJ/cm3), and its detonation pressure (128.95 GPa) and detonation velocity (13.60 km/s) are respectively about seven times and twice those of TNT, and it is therefore a promising high-energy-density material