Built-in Sensors Research Articles

The vibration knows who you are! A further analysis on usable authentication for smartwatch users

These days, smartwatches are becoming more common and can even operate in stand-alone mode. This increases the need for smartwatches to authenticate users independently without paired smartphones. Currently, password or pattern-based methods can authenticate the smartwatch users in stand-alone mode, but these methods are known to be vulnerable to simple attacks such as shoulder-surfing and password dictionary attacks. In addition, biometric-based methods, which are expected to release on smartwatches in the near future, require inconvenient user interaction or special sensors for measurement. In light of this, we propose a smartwatch user authentication method that does not require any additional sensors or user interaction. Based on the fact that the human body structure affects the way vibrations are absorbed, reflected, and propagated, we designed a smartwatch user authentication method based on a challenge-response structure using vibrations. In our method, a challenge is a set of fresh random vibrations, which are provided by default in current smartwatches, and a response to the challenge is measured by built-in gyroscope and accelerometer sensors. Our earlier study demonstrated that commercial smartwatch users can be authenticated with a low equal error rate (EER) of 1.37 %. In this paper, we extended the analysis of our method on various vibration types by using a prototype setup. As a result, we discovered an outperformed vibration type for user authentication. We conducted further analysis for users with heavier body weights as these individuals are more vulnerable to a not-in-wear attack. Finally, we conducted more advanced impersonation attacks on test participants with one or more similar physical indicators to demonstrate that our method is also secure against a wider range of more complex attacks.

Robust thermal error estimation for machine tools based on in-process multi-point temperature measurement of a single axis actuated by a ball screw feed drive system

In machine tools, thermal errors account for 70 % of the total machining errors. The latest improvements in sensor technology have enabled the robust estimation of thermal displacement using the multi-point temperature data of an entire structure. The complex geometry and constant motion of a feed drive system make the direct measurement of temperature difficult because of the sensor power supply and data transmission. In this study, a wireless multi-point temperature sensor system was developed, and the temperature of a ball screw was measured. The ball screw was developed with a built-in temperature sensor array and was installed in an actual machining center. Thermal error measurement tests were conducted, wherein the temperatures of 277 points on the structure and 25 points on the Z-axis ball screw were measured during machine operation. Using finite element analysis (FEA) with multi-point temperature mapping of the drive system, the difference between the relative displacement of the tool center point (TCP) along the Z-axis, measured by an eddy current sensor, and the value estimated by the FEA was calculated to be less than 10 μm. However, the Z-axis ball screw temperature distribution contributed little to the accuracy of thermal displacement estimation along other axes. Therefore, a superposition of the thermal displacements of the structure and drive system provided TCP relative displacement, suggesting the effect of temperature-dependent geometric errors on the volumetric error model of machine tools.

DeepVIP: Deep Learning-Based Vehicle Indoor Positioning Using Smartphones

The advent of sensor-rich smart devices (e.g., smartphones) has enabled a lot of applications and services. One of these applications and services is smartphone-based vehicle indoor positioning, which is a key technology for smart car parking and driverless cars. So far, most vehicle indoor positioning solutions either use infrastructures (e.g., WiFi access points) or inertial sensors, which suffer from low positioning accuracy, limited coverage, or high cost to deploy new equipment. To tackle these challenges, in this work we propose a novel Deep Learning-based Vehicle Indoor Positioning (DeepVIP) approach using smartphone built-in sensors, including accelerometer, gyroscope, magnetometer, and gravity sensor. Experiments are conducted in indoor parking areas. Experimental results show that the proposed method outperforms the state-of-the-art methods.

Combination of VSLAM and a Magnetic Fingerprint Map to Improve Accuracy of Indoor Positioning

With the continual advancement of positioning technology, people's use of mobile devices has increased substantially. The global navigation satellite system (GNSS) has improved outdoor positioning performance. However, it cannot effectively locate indoor users owing to signal masking effects. Common indoor positioning technologies include radio frequencies, image visions, and pedestrian dead reckoning. However, the advantages and disadvantages of each technology prevent a single indoor positioning technology from solving problems related to various environmental factors. In this study, a hybrid method was proposed to improve the accuracy of indoor positioning by combining visual simultaneous localization and mapping (VSLAM) with a magnetic fingerprint map. A smartphone was used as an experimental device, and a built-in camera and magnetic sensor were used to collect data on the characteristics of the indoor environment and to determine the effect of the magnetic field on the building structure. First, through the use of a preestablished indoor magnetic fingerprint map, the initial position was obtained using the weighted k-nearest neighbor matching method. Subsequently, combined with the VSLAM, the Oriented FAST and Rotated BRIEF (ORB) feature was used to calculate the indoor coordinates of a user. Finally, the optimal user's position was determined by employing loose coupling and coordinate constraints from a magnetic fingerprint map. The findings indicated that the indoor positioning accuracy could reach 0.5 to 0.7 m and that different brands and models of mobile devices could achieve the same accuracy.

Integrated sensor printed on the separator enabling the detection of dissolved manganese ions in battery cell

Conventional monitoring of Li-ion battery cell performance is carried out by combining empirical measurement of the extrinsic parameters with multipart modeling and approximation algorithms. A step forward would be enabling more reliable built-in sensing systems that allow collecting direct information, such as a degree of constituting materials degradation. Transition metal dissolution is one of the most severe degradation processes affecting the performance of the whole battery cell. It can be accelerated through different mechanisms, and its monitoring has been a topic of several studies in recent decades. In this work, we establish an approach for unambiguous detection of dissolved manganese ions via the built-in electrochemical sensor with scavenger moieties. We demonstrate that manganese ion-imprinted polymer (Mn(II)-IIP) deposited between two electrodes printed directly on the separator can be used as a sensing layer. The resistance of this sensing layer changes due to the coordination of the ion-imprinted polymer with dissolved manganese ions and this is then precisely monitored by the electrochemical impedance spectroscopy in the mid-frequency range. Both the electrodes and sensing layer remain stable within the voltage range of battery cycling over a longer application time. The sensor performance was validated in the single-layer pouch cell using Li||LiMn2O4 chemistry. The use of printing technology permits large-scale commercialization; sensors printed on the separator do not significantly alter the current production technology and, most importantly, have a negligible impact on the cell energy density. The approach is universal and can eventually be extended to the detection of other degradation products in the electrolyte.

QRsens: Dual-purpose quick response code with built-in colorimetric sensors

QRsens represents a family of Quick Response (QR) sensing codes for in-situ air analysis with a customized smartphone application to simultaneously read the QR code and the colorimetric sensors. Five colorimetric sensors (temperature, relative humidity (RH), and three gas sensors (CO2, NH3 and H2S)) were designed with the aim of proposing two end-use applications for ambient analysis, i.e., enclosed spaces monitoring, and smart packaging. Both QR code and colorimetric sensing inks were deposited by standard screen printing on white paper. To ensure minimal ambient light dependence of QRsens during the real-time analysis, the smartphone application was programmed for an effective colour correction procedure based on black and white references for three standard illumination temperatures (3000, 4000 and 5000 K). Depending on the type of sensor being analysed, this integration achieved a reduction of ∼71 – 87% of QRsens's dependence on the light temperature. After the illumination colour correction, colorimetric gas sensors exhibited a detection range of 0.7–4.1%, 0.7–7.5 ppm, and 0.13–0.7 ppm for CO2, NH3 and H2S, respectively. In summary, the study presents an affordable built-in multi-sensing platform in the form of QRsens for in-situ monitoring with potential in different types of ambient air analysis applications.

An Intelligent Medical Isolation Observation Management System Based on the Internet of Things.

Since COVID-19 (coronavirus disease 2019) was discovered in December 2019, it has spread worldwide. Early isolation and medical observation management of cases and their close contacts are the key to controlling the spread of the epidemic. However, traditional medical observation requires medical staff to measure body temperature and other vital signs face to face and record them manually. There is a general shortage of human and personal protective equipment and a high risk of occupational exposure, which seriously threaten the safety of medical staff. We designed an intelligent crowd isolation medical observation management system framework based on the Internet of Things using wireless telemetry and big data cloud platform remote management technology. Through a smart wearable device with built-in sensors, vital sign data and geographical locations of medical observation subjects are collected and automatically uploaded to the big data monitoring platform on demand. According to the comprehensive analysis of the set threshold parameters, abnormal subjects are screened out, and activity tracking and health status monitoring for medical observation and management objectives are performed through monitoring and early warning management and post-event data traceability. In the trial of this system, the subjects wore the wristwatches designed in this study and real-time monitoring was conducted throughout the whole process. Additionally, for comparison, the traditional method was also used for these people. Medical staff came to measure their temperature twice a day. The subjects were 1,128 returned overseas Chinese from Europe. Compared with the traditional vital sign detection method, the system designed in this study has the advantages of a fast response, low error, stability, and good endurance. It can monitor the temperature, pulse, blood pressure, and heart rate of the monitored subject in real time. The system designed in this study and the traditional vital sign detection method were both used to monitor 1,128 close contacts with COVID-19. There were six cases of abnormal body temperature that were missed by traditional manual temperature measurement in the morning and evening, and these six cases (0.53%) were sent to the hospital for further diagnosis. The abnormal body temperature of these six cases was not found in time when the medical staff came to check the temperature on a twice-a-day basis. The system designed in this study, however, can detect the abnormal body temperature of all these six people. The sensitivity and specificity of our system were both 100%. The system designed in this study can monitor the body temperature, blood oxygen, blood pressure, heart rate, and geographical location of the monitoring subject in real time. It can be extended to COVID-19 medical observation isolation points, shelter hospitals, infectious disease wards, and nursing homes.

Real-time assessment of asphalt pavement moduli and traffic loads using monitoring data from Built-in Sensors: Optimal sensor placement and identification algorithm

Asphalt pavement performance is gradually attenuated subjected to repeated traffic loads. Grasping the relationship between the repeated traffic loads and pavement modulus attenuation is crucial for understanding pavement mechanical behavior, improving pavement design methods, and guiding maintenance decisions. However, the current bottleneck is that neither the existing non-destructive testing nor monitoring methods can simultaneously obtain the information of traffic loads and pavement moduli. To fill this gap, an innovative real-time assessment method of asphalt pavement moduli and traffic loads from the aspect of structural health monitoring was proposed. On the one hand, the optimal sensor placement was determined by the theoretical relationships between moduli, loads, and mechanical responses based on elastic multi-layered theory. On the other hand, the corresponding algorithm for stepwise decoupling identification of asphalt pavement moduli and traffic loads to match the optimal sensor placement was developed. Therefrom, the validity of the proposed assessment method was theoretically verified, and the effects of the viscoelastic property of asphalt materials, measurement errors, and layer thickness on the identification accuracy were comprehensively discussed. The proposed assessment method was applied to the realistic pavement to further prove its applicability. Results show that the proposed assessment method can both evaluate pavement moduli and traffic loads accurately. The proposed assessment method has application prospects for simultaneous real-time monitoring of the pavement modulus attenuation and traffic loads, which helps understand the pavement damage behavior, improve pavement design methods, and guide maintenance decisions.

Toward Asphalt Pavement Health Monitoring With Built-In Sensors: A Novel Application to Real-Time Modulus Evaluation

Modulus is a critical parameter for evaluating the bearing capacity and remaining life of pavements. The prevalent modulus back-calculation method by using the pavement surface deflections captured from falling weight deflectometer (FWD) could characterize the overall bearing capacity of pavements. However, a non-uniqueness issue may occur when identifying the modulus of each layer from the perspective of mathematics. Alternatively, this paper presents a novel application to real-time modulus evaluation based on asphalt pavement health monitoring with built-in sensors. First, the sensor layout is optimized on the basis of the theoretical relationship between modulus and mechanical response inside the pavement. The proposed sensor layout, including input (random loads), output (mechanical responses), and environmental sensing modules, is illustrated in detail. Then, the procedure for real-time modulus evaluation is proposed, and the convergence and uniqueness of modulus evaluation results are investigated. Lastly, a case study of the realistic asphalt pavement health-monitoring system is conducted to demonstrate the effectiveness of the application to real-time modulus evaluation. The results indicate that the proposed modulus evaluation method has a potential to identify the modulus of each pavement structural layer in real time with a convergent and unique solution under a realistic traffic load. The evaluation process has the advantage of not interfering with traffic compared with the prevalent FWD-based modulus evaluation method. The pavement health monitoring with built-in sensors is recommended for newly built roads to facilitate long-term real-time performance assessment and maintenance decision making.

Using a Mobile Phone to Demonstrate Thermal Properties of Materials

Mobile phones are a widely used platform for educational apps, mobile health, and a variety of chemical tests. Here, we are working on a mobile phone-based physics lab (mPhysics) that uses a mobile phone’s capabilities to run simple physics experiments and demonstrations. While a mobile phone can be used to analyze magnetic and optical properties of materials using built-in sensors, thermal analysis has never been incorporated into a mobile phone. Here, we propose to integrate thermochromic sensing with the image processing conducted on a mobile phone for in-class or in-lab demonstration of thermal properties of materials. We make inexpensive and nontoxic materials based on polydimethylsiloxane (PDMS) mixed with a thermochromic pigment that changes color from blue to white when heated. This material can be used to visualize such phenomena as change of temperature, thermal conductivity, and heat capacity. Then, the smartphone camera and a custom app can be used to track local color changes and translate them into different thermal properties. We propose to use this approach for experimental education of high school and undergraduate students.

An Indoor 3-D Quadrotor Localization Algorithm Based on WiFi RTT and MEMS Sensors

With the development of quadrotor-based location services, accurate indoor quadrotor localization plays an important role in various applications. Tight fusion refers to the process of integrating multisensor data into state estimation for optimization, and finally obtaining pose information. In this article, we propose a novel tight fusion method for quadrotor localization by fusing the WiFi round-trip time (RTT) and built-in smartphone microelectromechanical sensors. Unlike existing 3-D localization frameworks, the key contribution of the proposed method is to integrate 3-D outlier detection, state estimation, coordinate frame alignment, and data fusion into a nonlinear filtering framework. Specifically, this method is divided into four main steps: 1) the coordinates of the mobile phone and the quadrotor are converted to the same coordinate system through the coordinate alignment method we propose; 2) the proposed outlier detection method is used to obtain the 3-D coordinates of the quadrotor based on WiFi RTT; 3) the WiFi RTT localization results are integrated into an error-state Kalman filter (ESKF) to perform the integrated localization of the quadrotors; and 4) a Rauch–Tung–Striebel (RTS) smoothing method is used to optimize the localization results. The experimental results demonstrate that the proposed method outperforms the classic localization method in terms of both accuracy and robustness.

Integrating a pressure sensor with an OCT handheld probe to facilitate imaging of microvascular information in skin tissue beds.

Optical coherence tomography (OCT) and OCT angiography (OCTA) have been increasingly applied in skin imaging applications in dermatology, where the imaging is often performed with the OCT probe in contact with the skin surface. However, this contact mode imaging can introduce uncontrollable mechanical stress applied to the skin, inevitably complicating the interpretation of OCT/OCTA imaging results. There remains a need for a strategy for assessing local pressure applied on the skin during imaging acquisition. This study reports a handheld scanning probe integrated with built-in pressure sensors, allowing the operator to control the mechanical stress applied to the skin in real-time. With real time feedback information, the operator can easily determine whether the pressure applied to the skin would affect the imaging quality so as to obtain repeatable and reliable OCTA images for a more accurate investigation of skin conditions. Using this probe, imaging of palm skin was used in this study to demonstrate how the OCTA imaging would have been affected by different mechanical pressures ranging from 0 to 69 kPa. The results showed that OCTA imaging is relatively stable when the pressure is less than 11 kPa, and within this range, the change of vascular area density calculated from the OCTA imaging is below 0.13%. In addition, the probe was used to augment the OCT monitoring of blood flow changes during a reactive hyperemia experiment, in which the operator could properly control the amount of pressure applied to the skin surface and achieve full release after compression stimulation.

Evaluation of the structural response of two in-service thin flexible pavements under heavy vehicle loading during different seasons by built-in sensors

ABSTRACT Long Heavy Vehicles (LHV) are considered more efficient and environmentally friendly transportation of goods compared to conventional trucks. Thus, the maximum allowable gross vehicle weight (GVW) in Sweden was increased on part of the road network from 64 to 74 tons in 2018 by increasing the vehicles’ length and the number of axle groups per vehicle but not the axle load limits. This change in loading conditions is expected to lead to changes in the structural response and degradation rate of thin pavements on the low-volume road network. To improve our understanding of thin pavements behaviour exposed to multiple axle loadings two thin pavement structures located in the north of Sweden were instrumented with road response and climate sensors. Four measurement campaigns were carried out within one year by in-situ stress and strain measurements from the built-in sensors as LHV passes over at normal speed. The recorded response was compared with numerical calculations based on multilayer elastic theory (MLET). Values of stresses and strains showed a generally good agreement with high values of coefficient of determination R 2 during different seasons when the asphalt stiffness values were adjusted based on temperature and granular layer stiffness values based on moisture.

Metabolic behaviour of Halomanas variabilis in a bio-reaction calorimeter during batch production of extracellular polymeric substances

The metabolic behaviour of Halomonas variabilis (HV) during the production of extracellular polymeric substances (EPS) in a bio-reaction calorimeter (BioRC) was analysed. From the batch mode experiment pH, turbidity, oxygen supplied, carbon dioxide emitted and heat evolved were monitored and measured online through in-built sensors. EPS produced, substrate (glucose) consumed and biomass produced was measured every four hours of the reaction. The reaction was carried out in optimized conditions and the EPS yield achieved was 4.74 g/L in 12.5 h. The Best carbon source for EPS production from HV was glucose when compared to fructose, sucrose and maltose. The specific growth rate was found to be 0.156 h−1. A relationship between the metabolic heat and the growth rate of HV during EPS production in BioRC was developed.

Sleep Bruxism Contributes to Motor Activity Increase during Sleep in Apneic and Nonapneic Patients-A Polysomnographic Study.

Background: Jaw motor activity (MA) in sleep bruxism (SB) has been demonstrated to accompany lower limb movements. However, it remains unknown whether SB activity coexists with other types of movements and what the possible underlying mechanisms of such temporal coexistence are. In obstructive sleep apnea (OSA), increased movement activity is also reported, including SB activity; however, no studies have compared MA in apneic and nonapneic SB patients. Aim: This cross-sectional study focused on the phenomenon of “big body movements” in patients with either SB or OSA (or both) and intended to identify the primary factors contributing to their appearance, using polysomnography (PSG) recording. Methods: A whole-night videoPSG was carried out in 287 participants, and 124 apneic and 146 nonapneic participants were selected for the study. In both groups, participants were further divided into no SB, moderate SB, and severe SB (SSB) subgroups based on their bruxism episode index (BEI). MA was recorded using a built-in sensor of the central PSG unit located on the participant’s chest during the examination. Results: The presence of SB was related to the higher intensity of MA in both apneic and nonapneic participants, though in general the MA level was higher in apneic participants, with the highest level observed in SSB apneic participants. Conclusions: SB might contribute to MA. The prevalence of SB might be higher in nonapneic patients due to phasic and mixed SB activity, whereas the SB phenotype seems to be less relevant in apneic patients. SB activity is likely to increase MA in non-REM 1 sleep.

A Continuous PDR and GNSS Fusing Algorithm for Smartphone Positioning

Pedestrian dead reckoning (PDR), used in state-of-the-art smartphones, calculates pedestrian positions by using built-in inertial sensors. However, the complex and changeable usage modes of smartphones have been obstructing the development of PDR in the field of gait detection. Since the measurement of the sensor is affected by noise, position errors will emerge, needing to be corrected periodically via external measurements. To this end, an optimization-based PDR (OBPDR) method for smartphones is proposed in this study. First, an improved finite state machine (IFSM) gait detection method is designed, which can improve the gait recognition rate and stability compared with the traditional peak detection method. Second, the step detection algorithm proposed in this paper is combined with a heading estimation to obtain the PDR dynamic model. Finally, the measurements of GNSS are fused to the PDR model, based on an adaptive extended Kalman filter (AEKF) algorithm, which can enhance the adaptability of the system to the environment in order to reduce the cumulative errors of PDR. Experiments are carried out to evaluate the performance of the proposed method. The results indicate that compared with the gait detection method, based on peak detection, and the integrated positioning method, based on an extended Kalman filter, the proposed method boasts favorable robustness and a high gait recognition rate, the recognition accuracy being kept between 97.5% and 98.5%; the average position error decreased by more than 67.25%.

Fluorometer Control and Readout Using an Arduino Nano 33 BLE Sense Board.

We describe the control and interfacing of a fluorometer designed for aerial drone-based measurements of chlorophyll-a using an Arduino Nano 33 BLE Sense board. This 64 MHz controller board provided suitable resolution and speed for analog-to-digital (ADC) conversion, processed data, handled communications via the Robot Operating System (ROS) and included a variety of built-in sensors that were used to monitor the fluorometer for vibration, acoustic noise, water leaks and overheating. The fluorometer was integrated into a small Uncrewed Aircraft System (sUAS) for automated water sampling through a Raspberry Pi master computer using the ROS. The average power consumption was 1.1 W. A signal standard deviation of 334 µV was achieved for the fluorescence blank measurement, mainly determined by the input noise equivalent power of the transimpedance amplifier. An ADC precision of 130 µV for 10 Hz chopped measurements was achieved for signals in the input range 0-600 mV.

OPTIMIZATION OF CUTTING MODES ON HEAVY MACHINES

The work is devoted to improvement of the design theory and development on this basis of adaptive hydrostatic supports of new generation, as well as development of methods for optimal design of spindle assemblies and guides with such supports. It is established that the use of fluid friction modes in heavily loaded nodes is very relevant for heavy numerically controlled precision machine tools. Hydrostatic spindle bearings are used in heavy lathes, in which the required accuracy of rotation is not provided by rolling bearings. An adaptive system for controlling the pressure in the pockets of hydrostatic supports and the tensioning force of the positioning drive has been developed. The system makes it possible to adjust the transfer function and exclude the possibility of oscillations by rather simple means. By means of the system the accuracy and productivity of machining on metal-cutting machines of different technological purposes is significantly increased. The reliability of the supports is increased, since the throttle elements are in motion during operation, which prevents the throttle gap from becoming overgrown. Stiffness and load-bearing capacity of the lubricant layer are increased. Debugging work on setting the working pressure in the support pockets is eliminated. The mechatronic system of adaptive control of pressure in the pockets of hydrostatic spindle assembly with increased accuracy is developed. Adaptive regulators of support power supply systems with spindle position feedback have been developed. A new design of hydrostatic bearing sleeve with built-in capacitive clearance sensors is developed

Characterizing the separation behavior of photocurable PDMS on a hydrogel film during vat photopolymerization: A benchmark study

The separation between printed part and vat floor in constrained surface vat photopolymerization (VP) is a long-standing challenge, which limits the printability of soft materials like polydimethylsiloxane (PDMS). This study aims to characterize the separation behavior of a photocurable PDMS resin on a hydrogel film whose high-water content is expected to minimize the separation force. For this benchmark study, a commercially available polytetrafluoroethylene (PTFE) and an acrylate-based photopolymer are used as the baselines. Along with two printing speeds, a total of eight cases (two films, two resins, and two speeds) are used in the design of the experiment. A modified digital light processing (DLP) printing with a built-in force sensor is used to sample the force profile during the printing. The results show that PDMS has a lower separation force but longer separation distance than those of the acrylate-based resin. The hydrogel film can further minimize the separation force and distance; those values are under 0.5 N in PDMS printing. A lower speed can further reduce the separation force, but increase separation distance, likely due to the rate-dependent material properties. These results suggest a potential solution to print PDMS fast with small and fragile features.

Partial Discharge Detection in Gas-Insulated Switchgears Using Sensors Integrated With UHF and Optical Sensing Methods

Partial discharge (PD) detection from protrusion defects is performed in an actual gas-insulated switchgears (GISs) based on a built-in sensor integrated with fluorescent fiber and ultrahigh frequency (UHF) antenna. The characteristics of both optical and UHF signals are analyzed in terms of inception voltage, pulse distribution, and statistical characteristics including the phase-resolved partial discharge (PRPD) pattern and PD number. In the experiment, protrusions with different curvatures are adopted to investigate the reason for the differences in the characteristics of UHF and optical signals. The results show that UHF and optical methods have different detection sensitivity for different discharge types owing to the different energies of electromagnetic waves and optical signals radiated in different discharge types. The optical method can easily detect avalanche discharge, while the UHF method can easily detect streamer discharge. This difference leads to the difference of the PD characteristics obtained by the two partial methods. For example, for the PD of small curvature protrusions, the PD inception voltage (PDIV) of the optical method is lower than that of the UHF method, because the defect triggers avalanche discharge. Moreover, the optical and UHF signal pulses are not always simultaneous in the time domain because they detect different discharge types. The PRPD patterns obtained by the optical method have a wider phase distribution than the UHF method because the optical method can detect avalanche discharge under low voltage.