Deep Learning Based Automatic Charging Identification and Positioning Method for Electric Vehicle

Electric vehicle charging identification and positioning is critically important to achieving automatic charging.In terms of the problem of automatic charging for electric vehicles,a dual recognition and positioning method based on deep learning is proposed.The method is divided into two parts:global recognition and localization and local recognition and localization.In the specific implementation process,the collected pictures of electric vehicle charging attitude are classified and labeled.It is trained with the improved YOLOv4 networkmodel and the corresponding detectionmodel is obtained.The contour of the electric vehicle is extracted by the BiSeNet semantic segmentation algorithm.The minimum external rectangle is used for positioning of the electric vehicle.Based on the location relationship between the charging port and the electric vehicle,the rough location information of the charging port is obtained.The automatic charging equipment moves to the vicinity of the charging port,and the camera near the charging gun collects pictures of the charging port.The model is detected by the Hough circle,the KM algorithmis used for featurematching,and the homography matrix is used to solve the attitude.The results show that the dual identification and location method based on the improved YOLOv4 algorithm proposed in this paper can accurately locate the charging port.The accuracy of the charging connection can reach 80%.It provides an effective way to solve the problems of automatic charging identification and positioning of electric vehicles and has strong engineering practical value.

IDENTIFICATION OF DOUBLE BOND POSITION IN HEXADECENYL ACETATES BY MASS SPECTROMETRY

A method for locating double bond in hexadecenyl acetates has been developed by analyzing the mass spectral patterns on a fuzzy classification. The procedure was tested with the spectra of Δ~2- to Δ^(15)-isomers and the original double-bond position in these acetates was located unambiguously.

Improved adaptive genetic algorithm based RFID positioning

The existing active tag-based radio frequency identi-fication(RFID)localization techniques show low accuracy in practical applications.To address such problems,we propose a chaotic adaptive genetic algorithm to align the passive tag ar-rays.We use chaotic sequences to generate the intersection points,the weakest single point intersection is used to ensure the convergence accuracy of the algorithm while avoiding the optimization jitter problem.Meanwhile,to avoid the problem of slow convergence and immature convergence of the algorithm caused by the weakening of individual competition at a later stage,we use adaptive rate of change to improve the optimiza-tion efficiency.In addition,to remove signal noise and outliers,we preprocess the data using Gaussian filtering.Experimental results demonstrate that the proposed algorithm achieves high-er localization accuracy and improves the convergence speed.

GEAR POSITION DECISION METHOD OF AUTOMATED MECHANICAL TRANSMISSION BASED ON FUZZY INFERENCE

A gear position decision method used in automated mechanical transmission is introduced. The algorithm of the mechod is composed of a driving environment and driver's intention estimator, the shift schedules suit for each typical driving environment and driver's intention situation, and an inference ligic to determine the most proper gear position for the present situation. The estimator identifies the driving environment and driver's intention features which are divided into some typical models. Based on the identified results, the algorithm works out the best gear position. It just simulates the course of driver's making gear position decision when driving a automobile with manual transmission. The test results show that the automated mechanical transmission with the method gives less unnecessary shifting and more proper gear position than common shift schedules.

Contributions of forensic anthropology to positive scientific identification:a critical Review

This review covers previous and current literature on the impact of forensic anthropologists on the positive scientific identification of human remains and aims to provide an under-standing of what information a forensic anthropologist can contribute to an investigation. Forensic anthropologists looking to identify human remains study traits of the skeleton and any orthopedic devices present. In order to obtain a positive scientific identification, evi-dence that is both sufficiently unique to the individual and comparable to available ante-mortem data from that individual must be found. The increased availability of radiographs, scans and implants in recent decades has facilitated the identification process. When these records are unavailable, other techniques, such as craniofacial superimposition and facial approximation, can be employed. While these methods may assist the identification process, they are most useful for exclusion of certain individuals and gathering leads from the public. Forensic anthropologists have heavily relied on the skull and its complexities for identifica-tion – typically focusing on the frontal sinus and other unique traits. Post-cranial remains can provide important information about bone density, possible disease and other character-istics that may also be utilized. Techniques used to positively identify individuals are not limited to medicolegal death investigations, and have been useful in other legal contexts. In the future, a team approach, utilizing all the information gathered by multiple forensic scientists–including forensic anthropologists–will most likely become more common.

Identifying migrant remains in South Texas:policy and practice

In 2012, Texas surpassed Arizona in migrant deaths. The majority of deaths occurred in the Rio Grande Valley, specifically in Brooks County, Texas. Brooks County is one of the poorest in the state and was overwhelmed with deaths, without appropriate resources to follow the state laws pertaining to the investigation of unidentified human remains. Until 2013, most remains that were not immediately identified were buried without collecting DNA samples and the location of burials was not recorded. Our paper outlines the difficulties searching for these burials, the struggles of the families of the missing, and the collaborative approaches to facilitating identifications in South Texas. Community outreach combined with geophysical surveys guide which cemeteries are in need of exhumations. Once ceme-teries are surveyed, archaeological methods are employed to exhume remains and docu-ment burials. Remains are taken to the Forensic Anthropology Center at Texas State for processing, analysis, and identification efforts. Undergraduate and graduate students clean remains and wash clothing and personal effects.After skeletal analysis, all information regarding the remains, including photographs of per-sonal effects, are uploaded to the National Missing and Unidentified Persons System (NamUs) and a DNA sample is submitted to the University of North Texas for inclusion in the Combined DNA Index System (CODIS) DNA database. However, CODIS lacks DNA family reference samples from many families of the missing due to families living outside the US or because they do not feel comfortable providing a DNA sample in the presence of law enforcement. Therefore, it is necessary to work with non-governmental organizations who specialize in collecting missing persons reports and DNA samples from the families of the missing. Working collaboratively with multiple agencies, identification of migrant remains is possible.