Safety Analysis of Riding at Intersection Entrance Using Video Recognition Technology

To study riding safety at intersection entrance,video recognition technology is used to build vehicle-bicycle conflict models based on the Bayesian method.It is analyzed the relationship among the width of nonmotorized lanes at the entrance lane of the intersection,the vehicle-bicycle soft isolation form of the entrance lane of intersection,the traffic volume of right-turning motor vehicles and straight-going non-motor vehicles,the speed of right-turning motor vehicles,and straight-going non-motor vehicles,and the conflict between right-turning motor vehicles and straight-going nonmotor vehicles.Due to the traditional statistical methods,to overcome the discreteness of vehicle-bicycle conflict data and the differences of influencing factors,the Bayesian random effect Poisson-log-normal model and random effect negative binomial regression model are established.The results show that the random effect Poisson-log-normal model is better than the negative binomial distribution of random effects;The width of non-motorized lanes,the form of vehicle-bicycle soft isolation,the traffic volume of right-turning motor vehicles,and the coefficients of straight traffic volume obey a normal distribution.Among them,the type of vehicle-bicycle soft isolation facilities and the vehicle-bicycle traffic volumes are significantly positively correlated with the number of vehicle-bicycle conflicts.The width of non-motorized lanes is significantly negatively correlated with the number of vehicle-bicycle conflicts.Peak periods and flat periods,the average speed of right-turning motor vehicles,and the average speed of straight-going non-motor vehicles have no significant influence on the number of vehicle-bicycle conflicts.

Video Recognition for Analyzing the Characteristics of Vehicle–Bicycle Conflict

Vehicle–bicycle conflict incurs a higher risk of traffic accidents,particularly as it frequently takes place at intersections.Mastering the traffic characteristics of vehicle–bicycle conflict and optimizing the design of intersections can effectively reduce such conflict.In this paper,the conflict between right-turning motor vehicles and straight-riding bicycles was taken as the research object,and T-Analyst video recognition technology was used to obtain data on riding(driving)behavior and vehicle–bicycle conflict at seven intersections in Changsha,China.Herein,eight typical traffic characteristics of vehicle–bicycle conflict are summarized,the causes of vehicle–bicycle conflict are analyzed using 18 factors in three dimensions,the internal relationship between intersection design factors and traffic conflicts is explored,and the guiding of design optimization based on the width of bicycle lanes and the soft separation between vehicles and bicycles is discussed.The results showed that colored paved bicycle lanes were better,performing better at a width of 2.5 m compared to 1.5 m.However,the colored pavement was not suitable for the entire road and had to be set at the position,at which the trajectories of a bicycle and motor vehicle overlapped.Thus,a 2.5-m-wide bicycle lane provides good safety.However,there are still defects in the existing safety indicators,so it is necessary to develop new indicators to reflect real vehicle–bicycle conflict situations more comprehensively.

Processing, Recovery and Analysis of Precious Metals Using Molecular Recogntion Technology

IBC Advanced Technologies' Molecular Recognition Technology(MRT) products,trade named SuperLig,selectively and rapidly bind with target metal ions to remove them from solution.The MRT process can produce a high purity separation product of maximum added value at low cost.In this paper,applications of MRT in the precious metals industry,including selective commercial separations involving Au,Pd,Pt,Rh,and Ru,are described and discussed.Application of MRT to the analytical determination of precious metals is presented.Potential use of MRT in recovering precious metals from end-of-life(EOL) products is discussed.

Organic heterojunction synaptic device with ultra high recognition rate for neuromorphic computing

Traditional computing structures are blocked by the von Neumann bottleneck,and neuromorphic computing devices inspired by the human brain which integrate storage and computation have received more and more attention.Here,a flexible organic device with 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene(C8-BTBT)and 2,9-didecyldinaphtho[2,3-b:2′,3′-f]thieno[3,2-b]thiophene(C10-DNTT)heterostructural channel having excellent synaptic behaviors was fabricated on muscovite(MICA)substrate,which has a memory window greater than 20 V.This device shows better electrical characteristics than organic field effect transistors with single organic semiconductor channel.Furthermore,the device simulates organism synaptic behaviors successfully,such as paired-pulse facilitation(PPF),long-term potentiation/depression(LTP/LTD)process,and transition from short-term memory(STM)to long-term memory(LTM)by optical and electrical modulations.Importantly,the neuromorphic computing function was verified using the Modified National Institute of Standards and Technology(MNIST)pattern recognition,with a recognition rate nearly 100%without noise.This research proposes a flexible organic heterojunction with the ultra-high recognition rate in MNIST pattern recognition and provides the possibility for future flexible wearable neuromorphic computing devices.

Seismic description and fluid identification of thin reservoirs in Shengli Chengdao extra-shallow sea oilfield

The meandering channel deposit of the upper member of Neogene Guantao Formation in Shengli Chengdao extra-shallow sea oilfield is characterized by rapid change in sedimentary facies.In addition,affected by surface tides and sea water reverberation,the double sensor seismic data processed by conventional methods has low signal-to-noise ratio and low resolution,and thus cannot meet the needs of seismic description and oil-bearing fluid identification of thin reservoirs less than 10 meters thick in this area.The two-step high resolution frequency bandwidth expanding processing technology was used to improve the signal-to-noise ratio and resolution of the seismic data,as a result,the dominant frequency of the seismic data was enhanced from 30 Hz to 50 Hz,and the sand body thickness resolution was enhanced from 10 m to 6 m.On the basis of fine layer control by seismic data,three types of seismic facies models,floodplain,natural levee and point bar,were defined,and the intelligent horizon-facies controlled recognition technology was worked out,which had a prediction error of reservoir thickness of less than 1.5 m.Clearly,the description accuracy of meandering channel sand bodies has been improved.The probability semi-quantitative oiliness identification method of fluid by prestack multi-parameters has been worked out by integrating Poisson’s ratio,fluid factor,product of Lame parameter and density,and other prestack elastic parameters,and the method has a coincidence rate of fluid identification of more than 90%,providing solid technical support for the exploration and development of thin reservoirs in Shengli Chengdao extra-shallow sea oilfield,which is expected to provide reference for the exploration and development of similar oilfields in China.

Metal separations of interest to the Chinese metallurgical industry

IBC Advanced Technologies’ Molecular Recognition Technology(MRT) SuperLig products selectively and rapidly bind with target species enabling their selective removal from solutions.The MRT process can produce a high purity separation product of maximum added value at a competitive cost.SuperLig products have high selectivity for many target species which can include metal ions,anions,and neutral molecules.In operation,the SuperLig product is first placed in a packed column.A solution containing a mixture of the target species and other chemical species is then passed through the column.The target species is removed selectively by the SuperLig product,the column is washed to remove residual feed solution,and the target species is recovered by a minimal quantity of eluent.The result is a pure and concentrated species that can be kept for its value or disposed of safely.The process is environmentally and ecologically friendly with no organic solvents being used.This paper provides a review of some examples of applications of MRT to separations of interest to the Chinese metallurgical industry.Included are several applications of MRT,including Pd separations from Pt metal refinery streams and low-grade spent catalyst wastes,Rh recovery from spent auto catalyst and other feeds,Re removal from selected impurity ions,Cd removal from Co electrolyte,Bi removal from Cu electrolyte,In and Ge separations from difficult matrices,and removal of bivalent first transition series and other metal ions from acid mine drainage(Berkeley Pit,Montana).Finally,the potential application of MRT to separations involving the recovery of rare earth metals and Li from low-level waste solutions and end-of-life products is discussed.

Hand Gesture-based Artificial Neural Network Trained Hybrid Human–machine Interface System to Navigate a Powered Wheelchair

Individuals with cerebral palsy and muscular dystrophy often lack fine motor control of their fingers which makes it difficult to control traditional powered wheelchairs using a joystick.Studies have shown the use of surface electromyography to steer powered wheelchairs or automobiles either through simulations or gaming controllers.However,these studies significantly lack issues with real world scenarios such as user’s safety,real-time control,and efficiency of the controller mechanism.The purpose of this study was to design,evaluate,and implement a hybrid human–machine interface system for a powered wheelchair that can detect human intent based on artificial neural network trained hand gesture recognition and navigate a powered wheelchair without colliding with objects around the path.Scaled Conjugate Gradient(SCG),Bayesian Regularization(BR),and Levenberg Marquart(LM)supervised artificial neural networks were trained in offline testing on eight participants without disability followed by online testing using the classifier with highest accuracy.Bayesian Regularization architecture showed highest accuracy at 98.4%across all participants and hidden layers.All participants successfully completed the path in an average of 5 min and 50 s,touching an average of 22.1%of the obstacles.The proposed hybrid system can be implemented to assist people with neuromuscular disabilities in near future.