Emergency Scheduling Optimization Simulation of Cloud Computing Platform Network Public Resources

Emergency scheduling of public resources on the cloud computing platform network can effectively improve the network emergency rescue capability of the cloud computing platform. To schedule the network common resources, it is necessary to generate the initial population through the Hamming distance constraint and improve the objective function as the fitness function to complete the emergency scheduling of the network common resources. The traditional method, from the perspective of public resource fairness and priority mapping, uses incremental optimization algorithm to realize emergency scheduling of public resources, neglecting the improvement process of the objective function, which leads to unsatisfactory scheduling effect. An emergency scheduling method of cloud computing platform network public resources based on genetic algorithm is proposed. With emergency public resource scheduling time cost and transportation cost minimizing target, initial population by Hamming distance constraints, emergency scheduling model, and the corresponding objective function improvement as the fitness function, the genetic algorithm to individual selection and crossover and mutation probability were optimized and complete the public emergency resources scheduling. Experimental results show that the proposed method can effectively improve the efficiency of emergency resource scheduling, and the reliability of emergency scheduling is better

Support vector machine filtering data aid on fatigue driving detection

This paper proposes an assumption that filtering out the confusing “awake” data from fatigue driving detection model promotes the accuracy of detection of “drowsy” status under real driving situation. Instead of focus on both “drowsy” and “awake” driving status, we set our first priority to alarm “drowsy” and temporarily ignore the accuracy of “awake” status recognition. The Support Vector Machine as a good classifier is employed for data filtering, provides more efficient training data and removes the data that may confuse the detection model. The results prove our assumption by 72% accuracy on “drowsy” recognition, which is higher than 38% recognition performed by detection without SVM filtering. In addition, the size of training samples after filtering for conducting detection model is extremely smaller than no filtering

Support vector machine filtering data aid on fatigue driving detection

This paper proposes an assumption that filtering out the confusing “awake” data from fatigue driving detection model promotes the accuracy of detection of “drowsy” status under real driving situation. Instead of focus on both “drowsy” and “awake” driving status, we set our first priority to alarm “drowsy” and temporarily ignore the accuracy of “awake” status recognition. The Support Vector Machine as a good classifier is employed for data filtering, provides more efficient training data and removes the data that may confuse the detection model. The results prove our assumption by 72% accuracy on “drowsy” recognition, which is higher than 38% recognition performed by detection without SVM filtering. In addition, the size of training samples after filtering for conducting detection model is extremely smaller than no filtering

Automatic Detection of Driver Fatigue Using Driving Operation Information for Transportation Safety

Fatigued driving is a major cause of road accidents. For this reason, the method in this paper is based on the steering wheel angles (SWA) and yaw angles (YA) information under real driving conditions to detect drivers’ fatigue levels. It analyzes the operation features of SWA and YA under different fatigue statuses, then calculates the approximate entropy (ApEn) features of a short sliding window on time series. Using the nonlinear feature construction theory of dynamic time series, with the fatigue features as input, designs a “2-6-6-3” multi-level back propagation (BP) Neural Networks classifier to realize the fatigue detection. An approximately 15-h experiment is carried out on a real road, and the data retrieved are segmented and labeled with three fatigue levels after expert evaluation, namely “awake”, “drowsy” and “very drowsy”. The average accuracy of 88.02% in fatigue identification was achieved in the experiment, endorsing the value of the proposed method for engineering applications

A pattern recognition for group abnormal behaviors based on Markov Random Fields energy

Group abnormal behaviors often occur abruptly under video surveillance, thus bringing serious consequences. How to recognize these behaviors correctly has always been the difficulty in research on intelligence video surveillance. This paper is based on the basic theory of Markov Random Fields to extract the features of those in video images, so as to recognize the group abnormal behaviors under video surveillance. Experiments show that this method can well reflect the real situation at the spot

A new biological visual cognitive behavioural modeling for video energy computing

As we all know, human vision is quite sensitive to abnormal behaviors, which is attributed to the discharging of the receptor cells in the brain visual cortex and the ensuing bioelectrical energy features. Inspired by this biological nature, this paper constructs a computing model to describe video dynamic energy, which can further improve the perception of machine vision to abnormal behaviors. Experiments show that this computing model can extract video energy features of abnormal behaviors under complex environment

A Finger Vein Image-Based Personal Identification System with Self-Adaptive Illuminance Control

As a biometric trait, finger vein pattern-based technology is highly effective for personal identification with high security. In this paper, we presented the design of a personal identification system based on near infrared (NIR) finger vein image. In this paper, we introduced an observation model of finger vein imaging, upon which a self-adaptive illuminance control algorithm is proposed and integrated into image acquisition hardware. According to the distribution of pixel intensity of the acquired image, the proposed algorithm could automatically adjust the illuminance distribution of lighting: increase the illuminance of lighting, under which the thicker part of finger body is presented and decrease the illuminance of lighting, under which the thinner part of finger body is presented. With this adaptation, the whole finger body could be illuminated appropriately according to its thickness distribution, and the overexposure and underexposure are avoided effectively. An NIR finger vein image database containing 2040 images is established and published in this paper. In the image preprocessing stage, Gabor filters are used to enhance captured raw finger vein images. In our experiment, the identification performance of our system is evaluated using the recognition rate and the margin distribution. A sparse representation-based algorithm is used to calculate the recognition rate and provide data for margin analysis. The results prove the effectiveness of the proposed illuminance control algorithm and the whole system in finger vein-based personal identification

Effect of Rare Earth Y on Microstructure and Properties of Sn-58Bi Solder Alloy

Sn-58Bi-xY alloys with different Y contents (x = 0.0%, 0.1%, 0.2%, 0.3%, 0.4%, 0.5% (mass fraction, the same below )) were fabricated in a vacuum furnace in nitrogen atmosphere. Microstructure, phase composition, melting characteristic, wettability and hardness of the alloys were investigated, and the influence of the rare earth Y on the formation of intermetallic compound among Sn-58Bi/Cu were analyzed, and the shear strength were test. The results show that the Sn-Bi microstructure is refined, and the microstructure of Sn-58Bi-xY is rich in Sn phase, Bi phase and eutectic microstructure of layered structure. The rare earth Y is distributed in rich Bi phase evenly. The melting points and melting ranges are less affected with the Y addition. The wettability of Sn-58Bi alloys reduces when the Y contents increase, but the hardness of Sn-58Bi alloys increases and reaches the maximum value 24.18HV when the content of Y is 0.4%. Rare earth Y can improve the shear strength of Sn-58Bi-xY/Cu solder joints, and the shear strength of the joints reach the maximum value 53.55 MPa when the content of Y is 0.2%. Y can promote the reaction of Sn-58Bi solder with Cu during welding and forming Cu6Sn5 intermetallic compound