Propagation of measurement accuracy to biomass soft-sensor estimation and control quality.
ABSTRACT: In biopharmaceutical process development and manufacturing, the online measurement of biomass and derived specific turnover rates is a central task to physiologically monitor and control the process. However, hard-type sensors such as dielectric spectroscopy, broth fluorescence, or permittivity measurement harbor various disadvantages. Therefore, soft-sensors, which use measurements of the off-gas stream and substrate feed to reconcile turnover rates and provide an online estimate of the biomass formation, are smart alternatives. For the reconciliation procedure, mass and energy balances are used together with accuracy estimations of measured conversion rates, which were so far arbitrarily chosen and static over the entire process. In this contribution, we present a novel strategy within the soft-sensor framework (named adaptive soft-sensor) to propagate uncertainties from measurements to conversion rates and demonstrate the benefits: For industrially relevant conditions, hereby the error of the resulting estimated biomass formation rate and specific substrate consumption rate could be decreased by 43 and 64 %, respectively, compared to traditional soft-sensor approaches. Moreover, we present a generic workflow to determine the required raw signal accuracy to obtain predefined accuracies of soft-sensor estimations. Thereby, appropriate measurement devices and maintenance intervals can be selected. Furthermore, using this workflow, we demonstrate that the estimation accuracy of the soft-sensor can be additionally and substantially increased.
Project description:The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users' hands has been captured, thereby greatly reducing the range of motion captured. This paper proposes a framework to estimate single-arm orientations using soft sensors mainly by combining a Bi-long short-term memory (Bi-LSTM) and two-layer LSTM. Positions of the two hands are measured using an HTC VIVE set, and the orientations of a single arm, including its corresponding upper arm and forearm, are estimated using the proposed framework based on the estimated positions of the two hands. Given that the proposed framework is meant for a single arm, if orientations of two arms are required to be estimated, the estimations are performed twice. To obtain the ground truth of the orientations of single-arm movements, two Myo gesture-control sensory armbands are employed on the single arm: one for the upper arm and the other for the forearm. The proposed framework analyzed the contextual features of consecutive sensory arm movements, which provides an efficient way to improve the accuracy of arm movement estimation. In comparison with the ground truth, the proposed method estimated the arm movements using a dynamic time warping distance, which was the average of 73.90% less than that of a conventional Bayesian framework. The distinct feature of our proposed framework is that the number of sensors attached to end-users is reduced. Additionally, with the use of our framework, the arm orientations can be estimated with any soft sensor, and good accuracy of the estimations can be ensured. Another contribution is the suggestion of the combination of the Bi-LSTM and two-layer LSTM.
Project description:Recent advances in 3D printing technology have enabled unprecedented design freedom across an ever-expanding portfolio of materials. However, direct 3D printing of soft polymeric materials such as polydimethylsiloxane (PDMS) is challenging, especially for structural complexities such as high-aspect ratio (>20) structures, 3D microfluidic channels (?150 ?m diameter), and biomimetic microstructures. This work presents a novel processing method entailing 3D printing of a thin-walled sacrificial metallic mold, soft polymer casting, and acidic etching of the mold. The proposed workflow enables the facile fabrication of various complex, bioinspired PDMS structures (<i>e.g.</i>, 3D double helical microfluidic channels embedded inside high-aspect ratio pillars) that are difficult or impossible to fabricate using currently available techniques. The microfluidic channels are further infused with conductive graphene nanoplatelet ink to realize two flexible piezoresistive microelectromechanical (MEMS) sensors (a bioinspired flow/tactile sensor and a dome-like force sensor) with embedded sensing elements. The MEMS force sensor is integrated into a Philips 9000 series electric shaver to demonstrate its application in "smart" consumer products in the future. Aided by current trends in industrialization and miniaturization in metal 3D printing, the proposed workflow shows promise as a low-temperature, scalable, and cleanroom-free technique of fabricating complex, soft polymeric, biomimetic structures, and embedded MEMS sensors.
Project description:Tactile sensors are essential if robots are to safely interact with the external world and to dexterously manipulate objects. Current tactile sensors have limitations restricting their use, notably being too fragile or having limited performance. Magnetic field-based soft tactile sensors offer a potential improvement, being durable, low cost, accurate and high bandwidth, but they are relatively undeveloped because of the complexities involved in design and calibration. This paper presents a general design methodology for magnetic field-based three-axis soft tactile sensors, enabling researchers to easily develop specific tactile sensors for a variety of applications. All aspects (design, fabrication, calibration and evaluation) of the development of tri-axis soft tactile sensors are presented and discussed. A moving least square approach is used to decouple and convert the magnetic field signal to force output to eliminate non-linearity and cross-talk effects. A case study of a tactile sensor prototype, MagOne, was developed. This achieved a resolution of 1.42 mN in normal force measurement (0.71 mN in shear force), good output repeatability and has a maximum hysteresis error of 3.4%. These results outperform comparable sensors reported previously, highlighting the efficacy of our methodology for sensor design.
Project description:Microelectromechanical systems (MEMS) resonant sensors provide a high degree of accuracy for measuring the physical properties of chemical and biological samples. These sensors enable the investigation of cellular mass and growth, though previous sensor designs have been limited to the study of homogeneous cell populations. Population heterogeneity, as is generally encountered in primary cultures, reduces measurement yield and limits the efficacy of sensor mass measurements. This paper presents a MEMS resonant pedestal sensor array fabricated over through-wafer pores compatible with vertical flow fields to increase measurement versatility (e.g., fluidic manipulation and throughput) and allow for the measurement of heterogeneous cell populations. Overall, the improved sensor increases capture by 100% at a flow rate of 2 μL/min, as characterized through microbead experiments, while maintaining measurement accuracy. Cell mass measurements of primary mouse hippocampal neurons in vitro, in the range of 0.1-0.9 ng, demonstrate the ability to investigate neuronal mass and changes in mass over time. Using an independent measurement of cell volume, we find cell density to be approximately 1.15 g/mL.
Project description:Designing efficient sensors for soft robotics aiming at human machine interaction remains a challenge. Here, we report a smart soft-robotic gripper system based on triboelectric nanogenerator sensors to capture the continuous motion and tactile information for soft gripper. With the special distributed electrodes, the tactile sensor can perceive the contact position and area of external stimuli. The gear-based length sensor with a stretchable strip allows the continuous detection of elongation via the sequential contact of each tooth. The triboelectric sensory information collected during the operation of soft gripper is further trained by support vector machine algorithm to identify diverse objects with an accuracy of 98.1%. Demonstration of digital twin applications, which show the object identification and duplicate robotic manipulation in virtual environment according to the real-time operation of the soft-robotic gripper system, is successfully created for virtual assembly lines and unmanned warehouse applications.
Project description:Increasing amounts of attention are being paid to the study of Soft Sensors and Soft Systems. Soft Robotic Systems require input from advances in the field of Soft Sensors. Soft sensors can help a soft robot to perceive and to act upon its immediate environment. The concept of integrating sensing capabilities into soft robotic systems is becoming increasingly important. One challenge is that most of the existing soft sensors have a requirement to be hardwired to power supplies or external data processing equipment. This requirement hinders the ability of a system designer to integrate soft sensors into soft robotic systems. In this article, we design, fabricate, and characterize a new soft sensor, which benefits from a combination of radio-frequency identification (RFID) tag design and microfluidic sensor fabrication technologies. We designed this sensor using the working principle of an RFID transporter antenna, but one whose resonant frequency changes in response to an applied strain. This new microfluidic sensor is intrinsically stretchable and can be reversibly strained. This sensor is a passive and wireless device, and as such, it does not require a power supply and is capable of transporting data without a wired connection. This strain sensor is best understood as an RFID tag antenna; it shows a resonant frequency change from approximately 860 to 800?MHz upon an applied strain change from 0% to 50%. Within the operating frequency, the sensor shows a standoff reading range of >7.5?m (at the resonant frequency). We characterize, experimentally, the electrical performance and the reliability of the fabrication process. We demonstrate a pneumatic soft robot that has four microfluidic sensors embedded in four of its legs, and we describe the implementation circuit to show that we can obtain movement information from the soft robot using our wireless soft sensors.
Project description:In bioprocesses, specific process responses such as the biomass cannot typically be measured directly on-line, since analytical sampling is associated with unavoidable time delays. Accessing those responses in real-time is essential for Quality by Design and process analytical technology concepts. Soft sensors overcome these limitations by indirectly measuring the variables of interest using a previously derived model and actual process data in real time. In this study, a biomass soft sensor based on 2D-fluorescence data and process data, was developed for a comprehensive study with a 20-L experimental design, for Escherichia coli fed-batch cultivations. A multivariate adaptive regression splines algorithm was applied to 2D-fluorescence spectra and process data, to estimate the biomass concentration at any time during the process. Prediction errors of 4.9% (0.99 g/L) for validation and 3.8% (0.69 g/L) for new data (external validation), were obtained. Using principal component and parallel factor analyses on the 2D-fluorescence data, two potential chemical compounds were identified and directly linked to cell metabolism. The same wavelength pairs were also important predictors for the regression-model performance. Overall, the proposed soft sensor is a valuable tool for monitoring the process performance on-line, enabling Quality by Design.
Project description:This paper presents a conceptual wind vector detector for measuring the velocity and direction of wind in enclosed or semi-enclosed large spaces. Firstly, a thermal wind sensor with constant power control was manufactured and then used as a wind velocity sensing unit. Secondly, a sensor bracket equipped with three thermal wind sensors was designed, the fluid dynamic response regularity of the measured wind field to the sensor bracket was analyzed using ANSYS Fluent CFD software, and then its structural parameters were optimized to improve measurement accuracy. The sensor bracket was fabricated via 3D printing. Finally, a unique wind vector measurement method was developed for the wind vector detector. Experimental results showed that the measured velocity range of the thermal wind sensor satisfied the requirements of being within 0-15 m/s with an accuracy of ±0.3 m/s, and the wind direction angle range of the wind vector detector was within 0-360° with an accuracy of ±5°. By changing the applied power control value of the thermal wind sensor and structural parameters of the sensor bracket, the measurement range and accuracy of the wind vector detector can be adjusted to suit different applications.
Project description:Long-term, continuous measurement of core body temperature is of high interest, due to the widespread use of this parameter as a key biomedical signal for clinical judgment and patient management. Traditional approaches rely on devices or instruments in rigid and planar forms, not readily amenable to intimate or conformable integration with soft, curvilinear, time-dynamic, surfaces of the skin. Here, materials and mechanics designs for differential temperature sensors are presented which can attach softly and reversibly onto the skin surface, and also sustain high levels of deformation (e.g., bending, twisting, and stretching). A theoretical approach, together with a modeling algorithm, yields core body temperature from multiple differential measurements from temperature sensors separated by different effective distances from the skin. The sensitivity, accuracy, and response time are analyzed by finite element analyses (FEA) to provide guidelines for relationships between sensor design and performance. Four sets of experiments on multiple devices with different dimensions and under different convection conditions illustrate the key features of the technology and the analysis approach. Finally, results indicate that thermally insulating materials with cellular structures offer advantages in reducing the response time and increasing the accuracy, while improving the mechanics and breathability.
Project description:State monitoring of the complex system needs a large number of sensors. Especially, studies in soft electronics aim to attain complete measurement of the body, mapping various stimulations like temperature, electrophysiological signals, and mechanical strains. However, conventional approach requires many sensor networks that cover the entire curvilinear surfaces of the target area. We introduce a new measuring system, a novel electronic skin integrated with a deep neural network that captures dynamic motions from a distance without creating a sensor network. The device detects minute deformations from the unique laser-induced crack structures. A single skin sensor decodes the complex motion of five finger motions in real-time, and the rapid situation learning (RSL) ensures stable operation regardless of its position on the wrist. The sensor is also capable of extracting gait motions from pelvis. This technology is expected to provide a turning point in health-monitoring, motion tracking, and soft robotics.