# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 2 Related Works
To test different velocity profiles, the profile calculation algorithm can be easily replaced. In the translational mechatronics system, the PID controller can follow any path with an error of less than 0.2%. By using the vibration-monitoring system, faults in a translational mechatronic system can be detected. It can be used as a type of preventative maintenance [28]. That paper focuses on real-time and online electrical parameter estimation using CMAC-ADALINE (cerebellar model articulation controller adaptive linear neuron) and the standard FOC (field-oriented control) scheme, the former being added to improve IM (induction motor) driver performance and lengthen the lifetime of the driver and induction motor. Using two types of neural networks, the authors estimated both rotor speed and rotor resistance for an induction motor. In that paper, they proposed a new kind of estimator for control schemes for neural networks and control theory. Moreover, the IM speed and rotor resistance estimations were validated, and the IM driver's performance was improved. A hybrid neural network estimator has been developed that takes advantage of two different types of neural network designs, and it has shown to be effective, as expected. By adjusting its weights, this estimator updates speed and rotor resistance, which improves the performance of the FOC algorithm. This algorithm was easily implemented in a real-time application over a three-phase IM, showing good speed and resistance tracking and a minimal error rate. Based on a backpropagation artificial neural network, the aforementioned paper presented a self-adjusting PID controller [28]. According to the desired output, the network calculates the appropriate gains according to the transient and stationary parts of the step response of the system. Besides using the error for network training, the maximum desired values of overshoots, settling times, and stationary errors were also used as input data for the network. In order to obtain the dynamic response data associated with PID gains, an offline training database was created using genetic algorithms. Genetic algorithms allow obtaining data in a wide range of operating ranges using only stable gains combinations. The database was used for training. Adapting to the error and the desired response, the neural network then estimated an appropriate gain combination. The method's performance was assessed by controlling the speed of a direct-current motor. The results show an average error rate of 4% between the database request and the response from the system. However, in 86% of the combinations of the test dataset (1544 connections), the results predicted by the network were not unstable [29]. In recent years, many studies, such as [23], have made use of deep learning and demonstrated its ability to prevent the sudden failure of lithium-ion batteries. Lithium-ion batteries (LIBs) have high efficiency and are low cost, but their instability and varying lifetimes remain challenges. Researchers have worked to develop ways of predicting the remaining useful life (RUL) of lithium-ion batteries, especially using data-driven approaches. A higher resolution of inter-cycle aging for faster and more accurate predictions, by considering temporal patterns and cross-data correlations in the raw data, specifically, terminal voltage, current, and cell temperature, is sought. An in-depth analysis of the deep learning models using the uncertainty metric, t-SNE of features, and various battery related tasks was presented in [23]. The proposed framework significantly boosted the remaining useful life prediction (25X faster) and resulted in a 10.6% mean absolute error rate. RUL predictions of LIBs are of great importance to the health management of electric vehicles and hybrid electric vehicles. Fluctuations and nonlinearity during battery degradation result in difficulties in both RUL prediction accuracy and model adaptability. To face the challenge, ref.