his paper presents an experiment for examining the ovalization of round-hole 7075-O aluminum alloy tubes submitted to cyclic bending. Here round-hole diameters of 2 to 10 mm were considered. It can be seen that the ovalization–curvature relationships demonstrated an asymmetrical, ratcheting, increasing and bow trend with the increase in the number of bending cycles. The hole diameter revealed a dramatic effect to this relationship. Furthermore, higher hole diameters led to larger tube’s ovalizations. Although five groups of round-hole 7075-O aluminum alloy tubes tested, the critical ovalization–controlled curvature relationships on a log-log scale exhibited nonparallel straight lines. Finally, a theoretical model was proposed for simulating the aforementioned relationships. The simulation results were compared with experimental test data, which showed generally good agreement.

Gravity method has tremendously penetrated widely the field of geophysical exploration being crucial and thus extensively discussed and vividly illustrated in this work.The gravity anomaly has been extensively discussed emphatically stressing the bouguer effects.The fundamental law of gravitational attraction is essential in delineating gravity data interpretation and the gravity effect or anomaly representation. A computational extension can be done based on the symmetric matrix representation with available gravity data and a vivid illustration for anomaly and residual computation has been made elsewhere and in some existing literatures.

Based on the geomagnetic, swan and intelligent technology such as video monitoring equipment to get the intersection of traffic basic data, using NARX neural network model to predict traffic flow, by analyzing the error of the model, and combined with the characteristics of the model itself, based on the genetic algorithm to optimize NARX neural network short-term intersection traffic flow forecasting model, the validity of the model and practical data verification, by verifying compared NARX neural network prediction model, based on the genetic algorithm to optimize and conventional NARX neural network to reduce the error of the mean, among them, turn left, straight decreased by 1.80% and 1.99% respectively, the average error indicates that the genetic neural network The complex not only speeds up the convergence speed, weight and threshold, but also greatly improves the prediction accuracy.

How to accurately describe the shape of the target is one of the difficult problems in image recognition. Aiming at this situation, a method of feature extraction based on morphology is proposed. Firstly, the image is processed by binarization and whitening, and then the target image is processed by morphological etching for many times. The morphological complexity of the corroded area divided by the length of the inner edge of the corroded area is taken as the recognition feature of the target image. At the same time, some other features are extracted as comparison, and the classification ability is analyzed by Fisher criterion. By decomposing the target image successively, the edge detail description and the overall shape approximation description of the target can be obtained simultaneously. The results of data processing show that the morphological method can effectively describe the shape features of objects and has good classification ability.

In Triple Layered Fuzzy Graph, It is discussed that, the Fuzzy Vertex and Edge Colouring and determine bounds for Fuzzy Chromatic Number and Fuzzy Index Number.

In Triple Layered Fuzzy Graph, It is discussed that, the Domination Parameters such as Domination Chromatic Number, Fuzzy Inverse Domination and Fuzzy Connected Inverse Domination.

In this paper, aiming at the timely collection of traffic information on roads, especially crossing roads, we propose a vehicle detection and tracking based on comprehensive features, which is realized by using UAV, so as to realize the collection of traffic information on crossing roads through the integration of "sky-ground". Real-time processing of video data received by the UAV, Otsu is used to continuously adjust the size of segmentation gray threshold of UAV in image processing at different heights. The frame difference method is used to realize the recognition and counting of vehicles in video, and then the real-time traffic flow on the road is calculated and extracted. The position, color and geometric features are integrated to find the position of the vehicle in the next frame, so as to realize the tracking of the target vehicle. Finally, the detection of vehicles on the current road and the extraction of their speed and track is realized, so as to realize the real-time monitoring of traffic state of the intersection. In the outdoor test, the method of vehicle tracking based on comprehensive features proposed in this paper was tested. The experimental results show that this method has higher accuracy than a single feature. Compared with machine learning and other methods to detect vehicles have a faster speed. Finally, the conclusion is that the method has high accuracy and speed, and its characteristics enable it to meet the needs of road vehicle detection under normal circumstances.

The present study aims to investigate vibration monitoring status in region three of gas transmission operation in Iran. Vibration monitoring is a strong tool for troubleshooting and protecting equipment (turbines). For this purpose, the vibration condition monitoring systems in a gas compression station have been studied. The number and location of vibration sensors, vibration signal transmission to the control room, alarm and stop command, and the ability to perform advanced vibration analysis for troubleshooting and data storage are taken into consideration. The favorable situation of vibration monitoring is provided for the purpose of comparison and conclusions about the status of vibration monitoring and needs have been made.

Aiming at the problems of slow detection speed, poor real-time performance and low accuracy in visual AGV obstacle detection in closed logistics storage environment. A visual AGV obstacle detection algorithm based on support vector machines (SVM) neural network in closed environment is proposed in this paper. The path of visual AGV is segmented under the logistic path, the training samples of SVM neural network are collected in batches, then the obstacle comparison dictionary based on the segmented path is established, and the obstacle detection and calibration are completed by using the image frame difference method. This paper designs an obstacle recognition experiment in closed environment. The experimental results show that the algorithm can effectively and quickly complete the visual AGV calibration and detection of obstacles in closed storage environment.

The article describes three methods for detecting vehicles using image processing techniques. The first is the differential method, which first uses the differential to the general position of the vehicle, then performs morphological processing to eliminate the noise and finally marks the specific location of the vehicle. The second is a machine learning method that trains positive and negative samples, obtains a classifier, and uses a classifier to perform vehicle detection. The third is Faster-RCNN, which first labels the sample, then uses Faster-RCNN to train the model, and finally uses the appropriate image to detect the model.

This paper aims on showing one of the many ways that flight crewmembers can be assigned flight legs to and from their home base; this known as the Crew Assignment problem. The paper aims to minimize the lay-over time of crew members at the when they are not in their home town and maximize their time in their home town using a type of Linear Programming Model known as the Hungarian Algorithm. The results of this algorithm can  confirmed using any other LP model.

A simple two-dimensional lampshade structure. The key components included in the lampshade components are light bulb, reflector, baffle, lens and housing. For the convenience of calculation and analysis, only the heat output on the surface of the bulb is considered, and the heat release of the filament in the bulb is not considered for now. The radiant energy of the bulb will contain all the thermal radiation, including visible and infrared radiation. Through the calculation of heat balance, the mathematical description of two-dimensional physical phenomena to describe the heat transfer kinetic model.

Abstract: In continuation to the study on formulation of average starting from Pythagorean means, Gauss developed one formulation of average from the definitions of arithmetic mean and geometric mean. This definition later on was termed as arithmetic–geometric mean (abbreviated as AGM). Recently, this formulation of average (namely AGM) has been applied in evaluating the value of parameter from observed data containing the parameter itself and random error. This paper describes that the arithmetic–geometric mean can be a technique of determining the value of parameter from observed data containing itself and random error. The underlying derivation of the technique along with some numerical applications has been presented in this paper.

In this paper, the impact of adding 0.2 % TiO₂ nanoparticles (NP) on the microstructural evolution, thermal behavior and tensile response of Sn-5%Sb-2 % In (SSI) ordinary solder was characterized by Optical Microscope (OM), Scanning electron microscope (SEM), XRD and EDS. The morphology results showed that adding TiO₂ NP resulted in the refinement of the formed SbSn, SbIn (IMCs) and the β-Sn grains were also markedly refined with the addition of TiO₂ NP. Impact of refinement caused by TiO₂ NP on the microstructure and tensile features of SSI alloy has been investigated. Impact of refinement caused by TiO₂ NP has been examined on the microstructural morphology and properties of Sn-5Sb-2In-TiO₂ (composite solder). SEM examinations indicated that SbSn IMC, have a cuboid structure. The morphology of SbSn was refined after including TiO₂ NP SbSn morphology was obviously refined. Differential Scanning Calorimetry (DSC) demonstrated minor increment within the dissolving temperature (ΔTm~ 0.66^o C) of SSI (composite). TiO₂ NP to the SSI makes composite joints to be progressively employable in plunging the event of the overall IMC particles (SbSn, and SbIn) within the scope of temperature utilized immediately.

: In recent years, with the rapid development of social economy and the rapid development of my country's transportation industry, the problem of traffic congestion has followed.  This paper takes Linzi District of Zibo City as an example. First, according to the survey data, macro basic map and road network traffic flow indicators, the operation status is divided into four levels; secondly, a prediction model based on NARX neural network is constructed, and finally the road network is operated.  Case analysis of state evaluation and road network traffic prediction, evaluation and feasibility verification based on data and models.  The results show that the model is effective.

With the increasing of traffic volume, the problem of urban road misshapen intersection becomes more and more prominent. Taking the intersection of donghuan road and yuxinsan road as an example, this paper expounds the design scheme of traffic organization improvement at the intersection. The results show that the vehicle saturation and parking delay of the intersection are improved after the optimal design scheme is adopted, which verifies the effectiveness of the scheme and provides a design idea for the intersection.

In order to improve the accuracy and stability of the vehicle in the path tracking process, this paper improves the model predictive controller. First, establish the vehicle dynamics model, determine the objective function, set the tire slip angle, front wheel angle, and center of mass slip angle as constraints, and design the model predictive controller. Secondly, the genetic algorithm is used to optimize the prediction time domain and the control time domain. The lateral tracking deviation, the yaw rate deviation and the centroid side slip angle deviation are simultaneously used as the optimization index items of the genetic algorithm to find the optimal control time domain and prediction time domain under different vehicle speeds. Finally, using matlab and carsim for joint simulation, the simulation results show that the improved model predictive controller can better track the reference trajectory, and has good stability and ride comfort.

This paper takes the driver as the research object, establishes a vehicle driving data set based on the driver’s emotions, builds a support vector machine (SVM) model to recognize the driver’s anger. And the effects of linear kernel function, polynomial kernel function, gaussian kernel function and sigmoid kernel function on the recognition result of SVM model are discussed separately. The results show that the sigmoid kernel function is completely unsuitable for driver anger recognition. The gaussian kernel function has the best recognition effect, which accuracy rate, recall rate and F1 score are significantly higher than other kernel functions, and the accuracy is not much different. Therefore, the SVM model based on the gaussian kernel function is more suitable for the recognition of the driver's anger emotion, and provides a theoretical basis for the driver's road anger early warning device.