Applying Set Membership Strategy in State of Charge Estimation for Lithium-ion Battery

As state of charge is one important variable to monitor the later battery management system, and as traditional Kalman filter can be used to estimate the state of charge for Lithium-ion battery on basis of probability distribution on external noise. To relax this strict assumption on external noise, set membership strategy is proposed to achieve our goal in case of unknown but bounded noise. External noise with unknown but bounded is more realistic than white noise. After equivalent circuit model is used to describe the Lithium-ion battery charging and discharging properties, one state space equation is constructed to regard state of charge as its state variable. Based on state space model about state of charge, two kinds of set membership strategies are put forth to achieve the state estimation, which corresponds to state of charge estimation. Due to external noise is bounded, i.e. external noise is in a set, we construct interval and ellipsoid estimation for state estimation respectively in case of external noise is assumed in an interval or ellipsoid. Then midpoint of interval or center of the ellipsoid are chosen as the final value for state of charge estimation. Finally, one simulation example confirms our theoretical results.

Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation

Home monitoring supports the continuous improvement of the therapy by sharing data with healthcare professionals. It is required when life-threatening events can still occur after hospital discharge such as neonatal apnea. However, multiple sources of external noise could affect data quality and/or increase the misdetection rate. In this study, we developed a mechatronic platform for sensor characterizations and a framework to manage data in the context of neonatal apnea. The platform can simulate the movement of the abdomen in different plausible newborn positions by merging data acquired simultaneously from three-axis accelerometers and infrared sensors. We simulated nine apnea conditions combining three different linear displacements and body postures in the presence of self-generated external noise, showing how it is possible to reduce errors near to zero in phenomena detection. Finally, the development of a smart 8Ws-based software and a customizable mobile application were proposed to facilitate data management and interpretation, classifying the alerts to guarantee the correct information sharing without specialized skills.

Spherical harmonic based noise rejection and neuronal sampling with multi-axis OPMs

In this study we explore the interference rejection and spatial sampling properties of multi-axis Optically Pumped Magnetometer (OPM) data. We use both vector spherical harmonics and eigenspectra to quantify how well an array can separate neuronal signal from environmental interference while adequately sampling the entire cortex. We found that triaxial OPMs have superb noise rejection properties allowing for very high orders of interference (L=6) to be accounted for while minimally affecting the neural space (2dB attenuation for a 60-sensor triaxial system). To adequately model the signals arising from the cortex, we show that at least 11th order (143 spatial degrees of freedom) irregular solid harmonics or 95 eigenvectors of the lead field are needed to model the neural space for OPM data (regardless of number of axes measured). This can be adequately sampled with 75-100 equidistant triaxial sensors (225-300 channels) or 200 equidistant radial channels. In other words, ordering the same number of channels in triaxial (rather than purely radial) configuration gives significant advantages not only in terms of external noise rejection but also minimizes cost, weight and cross-talk.

Research of road transport noise load in the central part of the City of Poltava

The studies were conducted in accordance with the requirements set out in the Declaration of the European Union "On Environmental Noise Assessment" and are closely related to finding solutions of the problems set out in the Law of Ukraine "On Ensuring Sanitary and Epidemic Welfare of the Population". The paper presents experimental and calculation studies on the assessment of noise pollution in the central part of Poltava. The results of such studies confirmed the hypothesis of exceeding the normative values of noise levels within some sections of the streets and directly at the intersections. It was found that the main causes of noise pollution include high intensity of public and light commercial transport means, large number of intersections and stops, poor road surface, as well as lack of acoustic protection, including lack of landscaping along roadsides. The research visually characterizes and investigates the boundaries of acoustic pollution areas distribution. Exceedence of the noise pollution normative values extends to the distance of up to 150 m from the experimental study points. The study determines the number of residents of the district who fall into high noise load areas and estimates the magnitude of risks to health of the citizens living within such areas. Experimental studies established a link between a negative impact of external noise generated by urban vehicles and urban residents' health state that requires hygienic research with application of WHO-recommended risk analysis methodology. It was found that almost 5,000 people from the study area spend most of their lives in the neighbourhoods where the noise level exceeds the permissible value of 55 dBA. Almost 2,000 of them live in the houses where penetrating noise exceeds the value of 40 dBA. Based on the results of theoretical provisions and conclusions, certain practical recommendations for management of noise pollution risks in the urban area were developed.

Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi

Measurement of electric charge on the battery in real-time cannot be separated from external noise and disturbances such as temperature and interference. An optimal State of Charge (SoC) estimator model is needed to make the estimation more accurate. To obtain the model, the battery was tested under room temperature conditions and at a temperature of 40oC to obtain a second-order RC model for the Li-Ion battery used. Based on the test data obtained, the data will be tested first using the Kalman Filter method to get an estimate of the State of Charge (SoC). Tests were carried out using MATLAB software. After the method was tested, the online SoC Estimator design began using the Raspberry Pi Single Board Computer (SBC). After that, the estimator will be tested first using data from offline measurements and then used in real-time (online) SoC estimation measurements. The Voc before the battery discharge test was 13.16 V and after the test, the measured Voc was 11.58 V. During the discharge the Voc was reduced by 1.58 V. While the discharge data from the battery manufacturer showed the reduced Voc during the discharge was 1.2V.

Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi

Measurement of electric charge on the battery in real-time cannot be separated from external noise and disturbances such as temperature and interference. An optimal State of Charge (SoC) estimator model is needed to make the estimation more accurate. To obtain the model, the battery was tested under room temperature conditions and at a temperature of 40oC to obtain a second-order RC model for the Li-Ion battery used. Based on the test data obtained, the data will be tested first using the Kalman Filter method to get an estimate of the State of Charge (SoC). Tests were carried out using MATLAB software. After the method was tested, the online SoC Estimator design began using the Raspberry Pi Single Board Computer (SBC). After that, the estimator will be tested first using data from offline measurements and then used in real-time (online) SoC estimation measurements. The Voc before the battery discharge test was 13.16 V and after the test, the measured Voc was 11.58 V. During the discharge the Voc was reduced by 1.58 V. While the discharge data from the battery manufacturer showed the reduced Voc during the discharge was 1.2V.

WPO-Net: Windowed Pose Optimization Network for Monocular Visual Odometry Estimation

Visual odometry is the process of estimating incremental localization of the camera in 3-dimensional space for autonomous driving. There have been new learning-based methods which do not require camera calibration and are robust to external noise. In this work, a new method that do not require camera calibration called the “windowed pose optimization network” is proposed to estimate the 6 degrees of freedom pose of a monocular camera. The architecture of the proposed network is based on supervised learning-based methods with feature encoder and pose regressor that takes multiple consecutive two grayscale image stacks at each step for training and enforces the composite pose constraints. The KITTI dataset is used to evaluate the performance of the proposed method. The proposed method yielded rotational error of 3.12 deg/100 m, and the training time is 41.32 ms, while inference time is 7.87 ms. Experiments demonstrate the competitive performance of the proposed method to other state-of-the-art related works which shows the novelty of the proposed technique.

Explorations of A Real-Time VR Emotion Prediction System Using Wearable Brain-Computer Interfacing

The following research describes the potential of using a four-class emotion classification using a four-channel wearable EEG headset combined with VR for evoking emotions from each individual. Multiple researchers have conducted and established emotion recognition by using a 2-D monitor screen for stimulus responses but this introduces artifacts such as the lack of concentration on-screen or external noise disturbance and the bulky and cumbersome wires on an EEG device were difficult and time-consuming to set up thus restricting to only the trained professionals to operate this complex and sensitive medical equipment. Therefore, using a small and portable EEG headset where it was accessible for consumers was used for the brainwave signal collection. The wearable EEG headset collects the brainwave samples at 256Hz at specific locations of the brain (Tp9, Tp10, AF7, AF8) and samples were transformed via FFT to obtain the five bands (Delta, Theta, Alpha, Beta, Gamma) and were classified using random forest classifier. An emotion prediction system was then developed and the trained model was used to benchmark the 4-class emotion prediction accuracy from each individual using a 4-channel low-cost EEG headset. Subsequently, a real-time prediction system was implemented and tested. Early findings showed that it could achieve predictions as high as 76.50% for intra-subject classification results.