[{"title":"( 51 个子文件 65.37MB ) IEEE_40+_clean_paper_of_Electric vehicle battery SOH.rar","children":[{"title":"IEEE_clean","children":[{"title":"IEEE_1","children":[{"title":"5_Lifecycle Prediction of Second Use Electric Vehicle Batteries Based on ARIMA Model.pdf <span style='color:#111;'> 351.62KB </span>","children":null,"spread":false},{"title":"4_State-of-Health Estimation for Lithium-Ion Batteries Based on the Multi-Island Genetic Algorithm and the Gaussian Process Regression.pdf <span style='color:#111;'> 4.51MB </span>","children":null,"spread":false},{"title":"8_State of Health Diagnosis and Remaining Useful Life Prediction for Lithium-ion Battery Based on Data Model Fusion Method.pdf <span style='color:#111;'> 4.24MB </span>","children":null,"spread":false},{"title":"10_Application of Matter Element Information Entropy and SVM in Lithium Battery Efficiency Evaluation and Prediction.pdf <span style='color:#111;'> 341.23KB </span>","children":null,"spread":false},{"title":"6_State-of-Health Estimation and Remaining-Useful-Life Prediction for Lithium-Ion Battery Using a Hybrid Data-Driven Method.pdf <span style='color:#111;'> 4.33MB </span>","children":null,"spread":false},{"title":"test.ipynb <span style='color:#111;'> 153.28KB </span>","children":null,"spread":false},{"title":"3_State of Health Prediction of Lithium-ion Batteries Using Accelerated Degradation Test Data.pdf <span style='color:#111;'> 277.85KB </span>","children":null,"spread":false},{"title":"12_Dynamic Bayesian Network based Lithium-ion Battery Health Prognosis for Electric Vehicles.pdf <span style='color:#111;'> 6.85MB </span>","children":null,"spread":false},{"title":"1_Chaotic Behavior of Battery State of Health.pdf <span style='color:#111;'> 484.20KB </span>","children":null,"spread":false},{"title":"new_abstract.csv <span style='color:#111;'> 35.41KB </span>","children":null,"spread":false},{"title":"2_Hybrid VARMA and LSTM Method for Lithium-ion Battery State-of-Charge and Output Voltage Forecasting in Electric Motorcycle Applications.pdf <span style='color:#111;'> 7.81MB </span>","children":null,"spread":false},{"title":"0_Data-driven SOH prediction for EV batteries.pdf <span style='color:#111;'> 336.76KB </span>","children":null,"spread":false},{"title":"9_State of charge and state of health estimation of lithium battery using dual Kalman filter method.pdf <span style='color:#111;'> 2.45MB </span>","children":null,"spread":false},{"title":"11_Remaining useful life prediction of electric vehicle lithium-ion battery based on particle filter method.pdf <span style='color:#111;'> 361.29KB </span>","children":null,"spread":false},{"title":"13_Development of Big Data Analytics Platform for Electric Vehicle Battery Management System.pdf <span style='color:#111;'> 297.97KB </span>","children":null,"spread":false},{"title":"7_Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries.pdf <span style='color:#111;'> 2.13MB </span>","children":null,"spread":false},{"title":"spider_test.pdf <span style='color:#111;'> 336.76KB </span>","children":null,"spread":false},{"title":"abstract.csv <span style='color:#111;'> 52.83KB </span>","children":null,"spread":false}],"spread":false},{"title":"IEEE_2","children":[{"title":"0_A Hybrid Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles.pdf <span style='color:#111;'> 4.86MB </span>","children":null,"spread":false},{"title":"8_Optical Fiber Intrusion Signal Recognition Based on Improved Mel Frequency Cepstrum Coefficient.pdf <span style='color:#111;'> 1.04MB </span>","children":null,"spread":false},{"title":"4_Vision Based Driver Assistance for Near Range Obstacle Sensing under Unstructured Traffic Environment.pdf <span style='color:#111;'> 3.23MB </span>","children":null,"spread":false},{"title":"9_A design method of EV charging security early warning model.pdf <span style='color:#111;'> 149.52KB </span>","children":null,"spread":false},{"title":"2_A Deep-Learning-Based Scheme for Detecting Driver Cell-Phone Use.pdf <span style='color:#111;'> 1.75MB </span>","children":null,"spread":false},{"title":"10_Analysis of directional antenna for railroad crossing safety applications.pdf <span style='color:#111;'> 794.27KB </span>","children":null,"spread":false},{"title":"5_Research on Calculation Method of Internal Resistance of Lithium Battery Based on Capacity Increment Curve.pdf <span style='color:#111;'> 416.20KB </span>","children":null,"spread":false},{"title":"11_A model based on hierarchical safety distance algorithm for ACC control mode switching strategy.pdf <span style='color:#111;'> 614.16KB </span>","children":null,"spread":false},{"title":"12_Visible light communication for V2V intelligent transport system.pdf <span style='color:#111;'> 843.47KB </span>","children":null,"spread":false},{"title":"test.ipynb <span style='color:#111;'> 193.57KB </span>","children":null,"spread":false},{"title":"new_abstract.csv <span style='color:#111;'> 36.62KB </span>","children":null,"spread":false},{"title":"6_Improving Pedestrian Safety in Cities Using Intelligent Wearable Systems.pdf <span style='color:#111;'> 5.73MB </span>","children":null,"spread":false},{"title":"13_Detection of abnormal moving vehicles for intelligent driver assistance system.pdf <span style='color:#111;'> 309.02KB </span>","children":null,"spread":false},{"title":"1_Design of Early Warning System for Fishing Vessels in Coastal Area.pdf <span style='color:#111;'> 596.75KB </span>","children":null,"spread":false},{"title":"abstract.csv <span style='color:#111;'> 49.11KB </span>","children":null,"spread":false},{"title":"abstract1.csv <span style='color:#111;'> 49.11KB </span>","children":null,"spread":false},{"title":"3_Research on calculation model of vehicle collision avoidance based on safe collision time.pdf <span style='color:#111;'> 4.39MB </span>","children":null,"spread":false},{"title":"7_Low-complexity Image-based Safety-Driving Assistant System for an Embedded Platform.pdf <span style='color:#111;'> 816.45KB </span>","children":null,"spread":false}],"spread":false},{"title":"IEEE_3","children":[{"title":"9_An optimization method of Voiceprint Recognition based on user portrait.pdf <span style='color:#111;'> 439.37KB </span>","children":null,"spread":false},{"title":"0_Research on tariff recovery risks assessment method based on electrical user portrait technology.pdf <span style='color:#111;'> 199.72KB </span>","children":null,"spread":false},{"title":"3_Exemplar-Based Portrait Style Transfer.pdf <span style='color:#111;'> 3.14MB </span>","children":null,"spread":false},{"title":"7_Research on User Behavioral Intention Based on Telecommunication Data.pdf <span style='color:#111;'> 294.72KB </span>","children":null,"spread":false},{"title":"test.ipynb <span style='color:#111;'> 183.51KB </span>","children":null,"spread":false},{"title":"new_abstract.csv <span style='color:#111;'> 28.06KB </span>","children":null,"spread":false},{"title":"6_An Algorithm for Describing User Behavior Model of Remote Sensing based on User Profile Technology.pdf <span style='color:#111;'> 3.17MB </span>","children":null,"spread":false},{"title":"11_Revealing Generalized Load Characteristics with Variable Scale User Portrait.pdf <span style='color:#111;'> 463.12KB </span>","children":null,"spread":false},{"title":"2_User Portraits and Investment Planning Based on Accounting Data.pdf <span style='color:#111;'> 2.00MB </span>","children":null,"spread":false},{"title":"4_Modeling of User Portrait Through Social Media.pdf <span style='color:#111;'> 1.23MB </span>","children":null,"spread":false},{"title":"10_A Deep Learning Methodology for Automatic Assessment of Portrait Image Aesthetic Quality.pdf <span style='color:#111;'> 5.24MB </span>","children":null,"spread":false},{"title":"8_User Portrait Technology Based on Stacking Mode.pdf <span style='color:#111;'> 440.49KB </span>","children":null,"spread":false},{"title":"5_Research on Situational Perception of Power Grid Business Based on User Portrait.pdf <span style='color:#111;'> 160.60KB </span>","children":null,"spread":false},{"title":"abstract.csv <span style='color:#111;'> 36.07KB </span>","children":null,"spread":false},{"title":"1_Research on Awareness Method of Cloud User Abnormal Behavior Based on Log Audit.pdf <span style='color:#111;'> 533.58KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}],"spread":true}]