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This study considers the performance of three object tracking algorithms, namely Circulant Structure of Tracking-by-Detection with Kernels (CSK), Distribution Fields for Tracking (DFT), and Locally Orderless Tracking (LOT). These methods are used to track containers in five video recordings of container loading and unloading. At each video frame change, the three methods are implemented to detect and track moving containers. The comparison is aimed to evaluate the tracking performances of the three methods for this instance by means of seven performance indexes, i.e., Frame per Second (FPS), Sample Accuracy, Average IoU, Average IoG, Object Tracking Accuracy (OTA), Precision, and Recall. The experimental results are presented in terms of the average accuracy, IoU, IoG, OTA, Precision, and Recall of the sample. Based on the results, tracking methods using DFT are recommended over those using CSK or LOT, even though its average frame rate is slower compared with CSK. Optimization is applied to each method by using simulated annealing to find their optimal parameters. The results show that DFT and LOT perform best, while CSK is not able to track the containers. DFT especially yields better performance on four of the videos. [ABSTRACT FROM AUTHOR]

The long-time test of battery discharge time is the main problem for implementing the performance requirements of the standard IEC 60086. Prediction of battery discharge time can be a solution for the long-time test problem. The discharge test data conceive valuable information of the nonlinear characteristics of the battery to build battery models. An intelligent system with learning algorithm to update the capability of the model from a set of training test data is suitable to predict the battery discharge time. The well-known ANFIS (Adaptive Neuro Fuzzy Inference Systems) is a neuro-adaptive system that has adaptive input nodes and has been applied to many prediction problems through learning and validation of the network from data. In this paper, the empirical test data related to the discharge time are acquired from the laboratory tests and are used to build the persuasive ANFIS model for the discharge time prediction from inputs to 48 recombination output data by means of modified membership functions. [ABSTRACT FROM AUTHOR]