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A Battery Management System (BMS) can prolong the life of the battery but it depends on the accuracy of the adopted scheme. Different techniques have been developed to enhance the BMS by monitoring the State of Health (SOH) of the battery. In this paper, the detection of battery voltage is analyzed by using the cycle counting method, which is a conventional technique and compared with Artificial Neural Network (ANN), a heuristic method. The advantage of the proposed ANN method is that SOH can be monitored without disconnecting the battery from the load. Also, the sampling data to the ANN are derived from various techniques including Open Circuit Voltage (OCV) method, Ambient temperature measurement, and valley point detection. A feed-forward backpropagation algorithm is used to achieve the purpose of real-time monitoring of the LAB. The results show that the precise estimation of SOH can be obtained by Feed-Forward Neural Network (FFNN) when trained with more sampling data.

New advancements in deep learning issues, motivated by real-world use cases, frequently contribute to this growth. Still, it’s not easy to recognize the speaker’s emotions from what they want to say. The proposed technique combines a deep learning-based brain-inspired prediction-making artificial neural network (ANN) through social ski-driver (SSD) optimization techniques. When assessing speaker emotion recognition (SER), the recognition results are compared with the existing convolutional neural network (CNN) and long short-term memory (LSTM)-based emotion recognition methods. The proposed method for classification based on ANN decreases the computational costs. The SER algorithm allows for a more in-depth classification of different emotions because of its relationship to ANN and LSTM. The SER model is based on ANN and the recognition impact of the feature reduction. The SER in this proposed research work is based on the ANN emotion classification system. Speaker recognition accuracy values of 96.46%, recall values of 95.39%, precision values of 95.21%, and F-Score values of 96.10% are obtained in this proposed result, which is higher than the existing result. The average accuracy results by using the proposed ANN classification technique are 4.38% and 2.89%, better than the existing CNN and LSTM techniques, respectively. The average precision results by using the proposed ANN classification technique are 4.67% and 2.49%, better than the existing CNN and LSTM techniques, respectively. The average recall results by using the proposed ANN classification technique are 2.90% and 1.42%, better than the existing CNN and LSTM techniques, respectively. The average precision results using the proposed ANN classification technique are 3.80% and 3.10%, better than the existing CNN and LSTM techniques, respectively.

The evolution of communication technologies with high-frequency radio-frequency (RF) devices increased the demand for compact and efficient designs. Micro-electromechanical systems (MEMS) technology revolutionized microwave and RF applications because of its ability to be engineered into miniaturized devices that are highly linear and power efficient. It is more challenging to perform numerical analysis and optimization of such complex MEMS devices. Electromagnetic (EM) simulation-based optimization software using different methodologies employing coupled domains and time domain analysis of MEMS devices requires repeated simulation, which makes it computationally expensive. The artificial neural network (ANN) model is an alternative to these conventional simulation-based design methodologies to expedite the design process. ANN models for RF and microwave modeling are known to be effective, precise, and flexible. ANN is capable of producing accurate results with less computational time than sophisticated EM models. An overview of various RF MEMS components and an introduction to ANN are provided in this chapter. In addition, this chapter presents the concept of modeling an RF MEMS shunt switch using ANN as a case study.

This paper aims at briefly overview the previous use of process modeling in denim manufacturing sectors, specifically, dyeing and finishing processes. A collection of relevant works is introduced, such as modeling the dyeing process for predicting the depth of shade, the effects of alkali reductive stripping process or cellulase washing process on color properties, and certain physical properties. A specific case in regard to modeling ozone fading denim by artificial neural networks (ANN) was studied as an example at the end, the results simply revealed that modeling process using soft computing techniques is capable to accurately predict the targeted outputs which is obviously promising and potential to make a difference in the future development of denim manufacturing.

BP neural network can achieve arbitrary nonlinear mapping of the input to the output, so it is extensively adopted in intelligent control, image recognition, hydrological predicting and water-resource quantity evaluation, etc., has stronger features of mapping, classification, functional fitting. This paper chooses the water quality of Lanzhou section of Yellow river as example by use of BP model to forecast the water quality. It is well verified that BP network model can reach the purposes of early warning and predicting.