Heterogeneous sensing in a multifunctional soft sensor for human-robot interfaces.

Erythromycin fermentation process is a typical microbial fermentation process. Soft sensors can be used to estimate biomass of Erythromycin fermentation process for their relative low cost, simple development, and ability to predict difficult-to-measure variables. However, traditional soft sensors, e.g. artificial neural network (ANN) soft sensors, support vector machine (SVM) soft sensors, etc., cannot represent the uncertainty (measurement precision) of outputs. That results in difficulties in practice. Gaussian process regression (GPR) provides a novel framework to solve regression problems. The output uncertainty of a GPR model follows Gaussian distribution, expressed in terms of mean and variance. The mean represents the predicted output. The variance can be viewed as the measure of confidence in the predicted output that distinguishes the GPR from NN and SVM soft sensor models. We proposed a systematic approach based on GPR and principal component analysis (PCA) to establish a soft sensor to estimate biomass of Erythromycin fermentation process. Simulations on industrial data from an Erythromycin fermentation process show the proposed GPR soft sensor has high performance of modeling the uncertainty of estimates.

Hyperelastic constitutive modeling of hydrogels based on primary deformation modes and validation under 3D stress states

Monitoring the process of curing of epoxy/graphite fiber composites with a recurrent neural network as a soft sensor

ABSTRACTReservoir characterization involves describing different reservoir properties quantitatively using various techniques in spatial variability. Nevertheless, the entire reservoir cannot be examined directly and there still exist uncertainties associated with the nature of geological data. Such uncertainties can lead to errors in the estimation of the ultimate recoverable oil. To cope with uncertainties, intelligent mathematical techniques to predict the spatial distribution of reservoir properties appear as strong tools. The goal here is to construct a reservoir model with lower uncertainties and realistic assumptions. Permeability is a petrophysical property that relates the amount of fluids in place and their potential for displacement. This fundamental property is a key factor in selecting proper enhanced oil recovery schemes and reservoir management. In this paper, a soft sensor on the basis of a feed‐forward artificial neural network was implemented to forecast permeability of a reservoir. Then, optimization of the neural network‐based soft sensor was performed using a hybrid genetic algorithm and particle swarm optimization method. The proposed genetic method was used for initial weighting of the parameters in the neural network. The developed methodology was examined using real field data. Results from the hybrid method‐based soft sensor were compared with the results obtained from the conventional artificial neural network. A good agreement between the results was observed, which demonstrates the usefulness of the developed hybrid genetic algorithm and particle swarm optimization in prediction of reservoir permeability.

In this study, a novel soil analysis method for fertilization recommendation was developed and validated with 161 soil samples taken from Turkey - Çarşamba plain for determination of potassium as a plant nutrient. In conventional soil analysis methods, available potassium (K) nutrient was determined by ammonium acetate extraction with flame photometer. In this study an alternative to existing method was proposed by developing extraction solutions suitable for interference dynamics of ion selective electrodes in a flow injection setup. Flow injection analysis system was optimized and K ion concentration of 161 soil samples taken from Turkey – Çarşamba plain was determined with potentiometrically. For the same soil samples, K+ ion concentration was determined with ammonium acetate extraction using flame photometer in parallel. Fertilization recommendations for potassium was calibrated on ammonium acetate extraction based measurements. In order to evaluate available potassium nutrient analysis results from new generation soil analysis method in fertilization recommendation process, a correlation model is required for relating new generation method results to conventional method results. An artificial neural network based soft sensor system was developed for this task. Potentiometric K+ ion measurement of soil sample in flow injection analysis system was presented as input to soft sensor system. Soft sensor predicted available K in soil sample based on artificial neural network model which can be used in fertilizer recommendation. Prediction performance of soft sensor was validated with experimental data and fitted with high correlation coefficient (R2= 0.902). Experimental studies have shown that K determined by potentiometric measurements can be used in fertilization recommendations in Çarşamba plain by using soft sensor approach.

This work introduces a dual-polarized (DP) slot-excited dielectric-filled cavity antenna phased array designed for 5G and beyond AiP solutions using low-temperature co-fired ceramic (LTCC) technology. The primary challenge is to maximize a two-dimensional (2-D) beamsteering range with a compact form factor in a DP operation. Most reported LTCC AiP arrays have ≤ 55° beamsteering range at 5G mmWave frequencies and lack experimental validation for DP 2-D beamsteering operation. In contrast, the proposed antenna element in the infinite array enables ±60° 2-D beamsteering in the 5G n261 (27.5-28.35 GHz) band with the form factor as small as 5mm×5mm×1.3mm. This performance is achieved by introducing a wide-angle impedance matching (WAIM) structure directly at the antenna aperture, comprising a dual-layer metasurface formed by periodically arranged electrically small patches in a staggered configuration. The antenna is jointly optimized with a DP feeding network to minimize parasitics in the multilayer LTCC stack and a co-integrated printed circuit board (PCB) carrier to mitigate package-carrier coupling often neglected in previous works. It is fabricated in a single LTCC process without air gaps to ensure precise layer-to-layer alignment and eliminate losses from additional support structures inherent to conventional air-gap WAIM stacks. A custom equivalent-circuit model enables efficient analysis of scan-dependent impedance matching for the optimization. A 128-element AiP prototype employs 4×4 subarrays of four DP elements on the PCB carrier and is packaged with ball grid arrays (BGAs) to facilitate integration. This modular approach allows scaling to large arrays while considering the critical effects of the package stack-up and DP feeding. The simulated active reflection coefficient is < -8.5 dB within ±60° (with a scan loss marginally above 3 dB) at 27.5-29.5 GHz. Measurements confirm simulations for all relevant performance metrics in ±60° 2D-range.

In a world of fast technological advancements, it is increasingly important to see how hydrocracking applications can benefit from and adapt to digitalization. A review of hydrocracking processes from the perspective of modeling and characterization methods is presented next to an investigation on digitalization trends. Both physics-based and data-based models are discussed according to their scope of use, needs, and capabilities based on open literature. Discrete and continuous lumping, structure-oriented lumping, and single event micro-kinetic models are reported as well as artificial neural networks, convolutional neural networks, and surrogate models. Infrared, near-infrared, ultra-violet and Raman spectroscopic methods are given with their examples for the characterization of feed or product streams of hydrocracking processes regarding boiling point curve, API, SARA, sulfur, nitrogen and metal content. The critical points to consider while modeling the system and the soft sensor are reported as well as the problems to be addressed. Optimization, control, and diagnostics applications are presented together with suggested future directions of interdisciplinary studies. The links required between the models, soft sensors, optimization, control, and diagnostics are suggested to achieve the automation goals and, therefore, a sustainable operation.

The concept of a soft sensor, a software replacement for unavailable, delayed or simply an expensive physical sensor, has received a lot of attention in the last decade. Its ability to estimate values of an observed phenomenon based on the expert knowledge defined in a form of theoretical or empirical models gives it a wide application and considerably lower costs. Furthermore, with the advent of artificial intelligence and data mining, soft sensors can be built purely by extracting correlations between different sensor data. However, both approaches can lead to either overfitting or overgeneralization. In this paper, both approaches are compared and combined in order to utilize the best of both worlds. Procedure for building soft sensors is defined and evaluated on a real-world use case for estimating daily solar radiation. The results show the best performance on the artificial neural network with RMSE of 1.454 and RRMSE of 3.42%.

Wastewater treatment plants (WWTPs) form an industry whose main goal is to reduce water’s pollutant products, which are harmful to the environment at high concentrations. In addition, regulations are applied by administrations to limit pollutant concentrations in effluent. In this context, control strategies have been adopted by WWTPs to avoid violating these limits; however, some violations still occur. For that reason, this work proposes the deployment of an artificial neural network (ANN)-based soft sensor in which a Long-Short Term Memory (LSTM) network is used to generate predictions of nitrogen-derived components, specifically ammonium () and total nitrogen (). is a limiting nutrient and can therefore cause eutrophication, while nitrogen in the form is toxic to aquatic life. These parameters are used by control strategies to allow actions to be taken in advance and only when violations are predicted. Since predictions complement control strategies, the evaluation of the ANN-based soft sensor was carried out using the Benchmark Simulation Model N.2. (BSM2) and three different control strategies (from low to high control complexity). Results show that our proposed method is able to predict nitrogen-derived products with good accuracy: the probability of detecting violations of BSM2’s limits is 86–94%. Moreover, the prediction accuracy can be improved by calibrating the soft sensor; for example, perfect prediction of all future violations can be achieved at the expense of increasing the false positive rate.

Wearable devices which monitor physiological measurements are of significant research interest for a wide number of applications including medicine, entertainment, and wellness monitoring. However, many wearable sensing systems are highly rigid and thus restrict the movement of the wearer, and are not modular or customizable for a specific application. Typically, one sensor is designed to model one physiological indicator which is not a scalable approach. This work aims to address these limitations, by developing soft sensors and including conductive particles into a silicone matrix which allows sheets of soft strain sensors to be developed rapidly using a rapid manufacturing process. By varying the morphology of the sensor sheets and electrode placement the response can be varied. To demonstrate the versatility and range of sensitivity of this base sensing material, two wearable sensors have been developed which show the detection of different physiological parameters. These include a pressure-sensitive insole sensor which can detect ground reaction forces and a strain sensor which can be worn over clothes to allow the measurements of heart rate, breathing rate, and gait.

In many soft sensor systems, external wires for connecting the sensors to control circuits have posed practical problems, in terms of compact form factors and physical robustness. This study proposes a design of soft sensor arrays that can operate with a drastically reduced number of wires without degrading the original performance. The proposed concept is an array of soft sensing modules, each of which consists of an inductor and a capacitor embedded next to the resistive sensing module, constructing a resistor-inductor-capacitor band-pass filter. By adjusting the values of the capacitance and the inductance, unique frequency band is assigned to each sensing module, enabling the distinctive detection using only two external wires regardless of the number of modules. The multi-touch sensing functionality of the system is analytically modeled and experimentally characterized. Taking advantage of the design, a fingertip tactile sensor and an insole foot pressure sensor are demonstrated as practical applications.

Development of soft sensor by incorporating the delayed infrequent and irregular measurements

Recently, head mounted displays based on holographic optical elements have been reported. These head mounted displays can be implemented in a compact form factor but the image delivered to user's each eye is still two-dimensional, which causes accommodation-vergence mismatch. In this paper, we propose a holographic three-dimensional head mounted display which displays holographic three-dimensional images to each eye. The proposed configuration can achieve a compact form factor by using holographic optical elements, and solve the accommodation-vergence mismatch by presenting holographic three-dimensional images to each eye.

Dual-stripline is gaining popularity in computer designs to save printed circuit board (PCB) cost and achieve more compact form factor. A key concern in dual-stripline design is the inter-layer crosstalk (ILC). In this paper, differential dual-stripline crosstalk is investigated, and a complete design strategy is provided. In addition to the conventional crosstalk mitigation techniques, an innovative wiring technique is proposed to reduce ILC for parallel dual-striplines. The proposed routing strategy can effectively mitigate the impact of ILC, and, as a result, enable high-density PCB layout, achieve compact form factor, and save the bill of material (BOM) cost.

Positive climate effects when AR customer support simultaneous trains AI experts for the smart industries of the future