Remaining useful life (RUL) prediction of lithium-ion batteries can reduce the risk of battery failure by predicting the end of life. In this paper, we propose novel RUL prediction techniques based on long short-term memory (LSTM). To estimate RUL even in the presence of capacity regeneration phenomenon, we consider multiple measurable data from battery management system such as voltage, current and temperature charging profiles whose patterns vary as aging. Unlike the traditional LSTM prediction that matches input layer with output layer as one-to-one structure, we leverage many-to-one structure to be flexible for various input types and to substantially reduce the number of parameters for better generalization. Using the NASA lithium-ion battery datasets, we verify the accuracy of the proposed LSTM-based RUL prediction. The experimental results show that the proposed single-channel LSTM model improves the mean absolute percentage error (MAPE) by 39.2% compared to the baseline LSTM model. Furthermore, the proposed multi-channel LSTM model significantly improves the MAPE, e.g., by 63.7% compared to the baseline; the proposed model achieves 0.47-1.88% of MAPE while the state-of-the-art baseline LSTM shows 0.6-6.45% of MAPE. INDEX TERMS Lithium-ion battery, long short-term memory, remaining useful life, capacity estimation.

Prognostics and health management is a promising methodology to cope with the risks of failure in advance and has been implemented in many well-known applications including battery systems. Since the estimation of battery capacity is critical for safe operation and decision making, battery capacity should be estimated precisely. In this regard, we leverage measurable data such as voltage, current, and temperature profiles from the battery management system whose patterns vary in cycles as aging. Based on these data, the relationship between capacity and charging profiles is learned by neural networks. Specifically, to estimate the state of health accurately we apply feedforward neural network, convolutional neural network, and long short-term memory. Our results show that the proposed multi-channel technique based on voltage, current, and temperature profiles outperforms the conventional method that uses only voltage profile by up to 25%-58% in terms of mean absolute percentage error. INDEX TERMS Lithium-ion battery, neural network, remaining useful life, capacity estimation, state of health.

This study assessed the method of fluid infusion control using an IntraVenous Infusion Controller (IVIC). Four methods of infusion control (dial flow controller, IV set without correction, IV set with correction and IVIC correction) were used to measure the volume of each technique at two infusion rates. The infused fluid volume with a dial flow controller was significantly larger than other methods. The infused fluid volume was significantly smaller with an IV set without correction over time. Regarding the concordance correlation coefficient (CCC) of infused fluid volume in relation to a target volume, IVIC correction was shown to have the highest level of agreement. The flow rate measured in check mode showed a good agreement with the volume of collected fluid after passing through the IV system. Thus, an IVIC could assist in providing an accurate infusion control.

No abstract

No abstract

No abstract

No abstract

Solar power is an important renewable energy resource that plays a pivotal role in replacing fossil fuel generators and lowering carbon emissions. Since sunlight, which is highly dependent on meteorological factors, is highly volatile, the difficulty in collecting real-time data from renewable energy power plants poses a major threat to maintaining the stability of the entire power system in the target area. A high-performance wireless metering modem is required to monitor the renewable energy generation power of the entire target area in real-time. However, installing such devices on all sites is expensive, so we propose a system that uses deep learning to estimate the generation power of a target site based on the power generations of some sample sites. We use clustering and distance-based sampling to extract a sample site corresponding to each target site and use the recurrent neural network (RNN)-based attention techniques to estimate the generation of target sites from the sample sites. Our experiments show that the proposed RNN-based attention models significantly improve estimation accuracy compared to the baseline model or other deep learning models, irrespective of the number or location of sample sites.INDEX TERMS deep learning, real-time estimation, attention, long short-term memory, solar power generation estimation.