Laws of strata energy release and corresponding safety warning system in metal mine

The strata deformation in mining area was monitored in Dabaoshan copper-iron mine,and an analytical method of strata energy release was put forward.On the basis of chaotic theory,by reconstructing the phase space for time series data of strata energy release,the saturated embedding dimension and the correlation dimension of the dynamic system were obtained to be 4 and 1.212 8,respectively,and the evolution laws of distances between phase points of strata energy release in the phase space were revealed.With grey theory,a prediction model of strata energy release was set up,the maximum error of which was less than 6.7%.The results show that there are chaotic characters in strata energy release during mining;after reconstructing phase space,the subtle changing characteristics of energy release can be magnified,and the internal rules can be fully demonstrated.According to the laws,a warning system for strata stability in mining area was established to provide a technical safeguard for safe mining.

A single dual-mode gas sensor for early safety warning of Li-ion batteries:Micro-scale Li dendrite and electrolyte leakage

Li dendrites and electrolyte leakage are common causes of Li-ion battery failure.H_(2),generated by Li dendrites,and electrolyte vapors have been regarded as gas markers of the early safety warning of Li-ion batteries.SnO_(2)-based gas sensors,widely used for a variety of applications,are promising for the early safety detection of Li-ion batteries,which are necessary and urgently required for the development of Li-ion battery systems.However,the traditional SnO_(2)sensor,with a single signal,cannot demonstrate intelligent multi-gas recognition.Here,a single dual-mode(direct and alternating current modes)SnO_(2)sensor demonstrates clear discrimination of electrolyte vapors and H_(2),released in different states of Li-ion batteries,together with principal component analysis(PCA)analysis.This work provides insight into the intelligent technology of single gas sensors.

Coal mine safety production forewarning based on improved BP neural network

Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.

Micro-behavior Analysis of Pilots Based on Human-Aircraft-Environment Interaction

Micro-behavior of pilots is one of the most remarkable aspects in flight safety research domain.The study of pilot’s micro-behavior and its function are of great significance to enhance active safety warnings of flight and evaluation of flight cadets.Based on the cognitive process of pilots,this paper explores the meanings and contents of previous research on the pilot’s micro-behavior.The history and research status of pilot’s micro-behavior are briefly introduced from the perspective of their psychology,physiology and physics.The current reviews mainly include the pilot’s characteristic,multi-information fusion,integrated cognitive and humanization about controlling environment,etc.The several methods of these studies are discussed,and the mechanisms,experimental contents and applicable conditions of pilot’s physiological,psychological and physical characteristics are analyzed.Meanwhile,the advantages and shortcomings of the existing research results are pointed out and analyzed.Combined with flight simulation experiment,the internal mechanism of pilot is explained.Furthermore,with the latest research in the modern flight field,and also from the specialization of application,the diversification of methodologies and the depth of investigation are provided,as well as the development trend of pilot’s micro-behavior analysis in the future.

Dangerous Driving Behavior Recognition and Prevention Using an Autoregressive Time-Series Model

Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.