Search Results (383)

This paper proposes a metrologically interpretable soft sensing method for estimating the liquid flow rates in hydraulic systems from non-invasive vibration frequency power band data. Despite considerable interest in non-invasive flow estimation, state-of-the-art methods provide little to no metrological capabilities. In this work, a dedicated test rig was developed to automatically acquire vibration and flow rate data from a centrifugal pump, in a flow rate range between 0.05 × 10⁻⁵${\mathrm{m}}^3$/$\mathrm{s}$ and 9.11 × 10⁻⁵${\mathrm{m}}^3$/$\mathrm{s}$. The vibration data were processed into power bands, which were subsequently used to optimize and train a multilayer perceptron neural network for flow soft sensing. The trained model was compared with models with different vibration processing methods from literature. The power band processing model resulted in a root mean squared error 75.4% smaller than the second-best model in cross-validation, and 51.5% smaller with test data. The uncertainty of the proposed regression model was estimated using a combination of ensemble learning and Monte Carlo simulations, and combined with the reference flow sensor uncertainty to obtain the total combined uncertainty of the soft sensor, found to be between 3.9 × 10⁻⁶${\mathrm{m}}^3$/$\mathrm{s}$ and 6.1 × 10⁻⁶${\mathrm{m}}^3$/$\mathrm{s}$ throughout the measured flow range. The reference flow sensor accuracy was found to be the largest individual contribution for the final uncertainty, closely followed by the regression model uncertainty. Full article

This paper presents a novel soft crawling robot controlled by gesture recognition, aimed at enhancing the operability and adaptability of soft robots through natural human–computer interactions. The Leap Motion sensor is employed to capture hand gesture data, and Unreal Engine is used for gesture recognition. Using the UE4Duino, gesture semantics are transmitted to an Arduino control system, enabling direct control over the robot’s movements. For accurate and real-time gesture recognition, we propose a threshold-based method for static gestures and a backpropagation (BP) neural network model for dynamic gestures. In terms of design, the robot utilizes cost-effective thermoplastic polyurethane (TPU) film as the primary pneumatic actuator material. Through a positive and negative pressure switching circuit, the robot’s actuators achieve controllable extension and contraction, allowing for basic movements such as linear motion and directional changes. Experimental results demonstrate that the robot can successfully perform diverse motions under gesture control, highlighting the potential of gesture-based interaction in soft robotics. Full article

Tactile perception plays a crucial role in the perception of products and consumer preferences. This perception process is structured in hierarchical layers comprising a sensory layer (soft and smooth) and an affective layer (comfort and luxury). In this study, we attempted to predict the evaluation score of sensory and affective tactile perceptions of materials using a biomimetic multimodal tactile sensor that mimics the active touch behavior of humans and measures physical parameters such as force, vibration, and temperature. We conducted sensory and affective descriptor evaluations on 32 materials, including cosmetics, textiles, and leather. Using the physical parameters obtained by the biomimetic multimodal tactile sensor as explanatory variables, we predicted the scores of the sensory and affective descriptors in 10 regression models. The bagging regressor demonstrated the best performance, achieving a coefficient of determination (R²) of >0.6 for fourteen of nineteen sensory and eight of twelve affective descriptors. The present model exhibited particularly high prediction accuracy for sensory descriptors such as “moist” and “elastic”, and for affective descriptors such as “pleasant” and “like”. These findings suggest a method to support efficient tactile design in product development across various industries by predicting tactile descriptor scores using physical parameters from a biomimetic tactile sensor. Full article

This study presents a comprehensive workflow for developing and deploying Multi-Layer Perceptron (MLP)-based soft sensors on embedded FPGAs, addressing diverse deployment objectives. The proposed workflow extends our prior research by introducing greater model adaptability. It supports various configurations—spanning layer counts, neuron counts, and quantization bitwidths—to accommodate the constraints and capabilities of different FPGA platforms. The workflow incorporates a custom-developed, open-source toolchain ElasticAI.Creator that facilitates quantization-aware training, integer-only inference, automated accelerator generation using VHDL templates, and synthesis alongside performance estimation. A case study on fluid flow estimation was conducted on two FPGA platforms: the AMD Spartan-7 XC7S15 and the Lattice iCE40UP5K. For precision-focused and latency-sensitive deployments, a six-layer, 60-neuron MLP accelerator quantized to 8 bits on the XC7S15 achieved an MSE of 56.56, an MAPE of 1.61%, and an inference latency of 23.87 $\mu$s. Moreover, for low-power and energy-constrained deployments, a five-layer, 30-neuron MLP accelerator quantized to 8 bits on the iCE40UP5K achieved an inference latency of 83.37 $\mu$s, a power consumption of 2.06 mW, and an energy consumption of just 0.172 $\mu$J per inference. These results confirm the workflow’s ability to identify optimal FPGA accelerators tailored to specific deployment requirements, achieving a balanced trade-off between precision, inference latency, and energy efficiency. Full article

Authenticating poppy seed oil is essential to ensure product quality and prevent economic and health-related fraud. This study developed a non-targeted approach using FT-IR spectroscopy and pattern recognition analysis to verify the authenticity of poppy seed oil. Thirty-nine poppy seed oil samples were sourced from online stores and local markets in Turkiye. Gas chromatography–Flame Ionization Detector (GC-FID) analysis revealed adulteration in 23% of the samples, characterized by unusual fatty acid composition. Spectra of the oil samples were captured with a portable 5-reflection FT-IR sensor. Soft Independent Model of Class Analogies (SIMCA) was used to create class algorithms, successfully detecting all instances of adulteration. Partial least square regression (PLSR) models were then developed to predict the predominant fatty acid composition, achieving strong external validation performance (R_CV = 0.96–0.99). The models exhibited low standard errors of prediction (SEP = 0.03–1.40%) and high predictive reliability (RPD = 2.9–6.1; RER = 8.4–13.1). This rapid, non-destructive method offers a reliable solution for authenticating poppy seed oil and predicting its fatty acid composition, presenting valuable applications for producers and regulatory authorities. This approach aids in regulatory compliance, protection of public health, and strengthening of consumer confidence by ensuring the authenticity of the product. Full article

Uncertainty-aware soft sensors in sign language recognition (SLR) integrate methods to quantify and manage the uncertainty in their predictions. This is particularly crucial in SLR due to the variability in sign language gestures and differences in individual signing styles. Managing uncertainty allows the system to handle variations in signing styles, lighting conditions, and occlusions more effectively. While current techniques for handling uncertainty in SLR systems offer significant benefits in terms of improved accuracy and robustness, they also come with notable disadvantages. High computational complexity, data dependency, scalability issues, sensor and environmental limitations, and real-time constraints all pose significant hurdles. The aim of the work is to develop and evaluate a Type-2 Neutrosophic Hidden Markov Model (HMM) for SLR that leverages the advanced uncertainty handling capabilities of Type-2 neutrosophic sets. In the suggested soft sensor model, the Foot of Uncertainty (FOU) allows Type-2 Neutrosophic HMMs to represent uncertainty as intervals, capturing the range of possible values for truth, falsity, and indeterminacy. This is especially useful in SLR, where gestures can be ambiguous or imprecise. This enhances the model’s ability to manage complex uncertainties in sign language gestures and mitigate issues related to model drift. The FOU provides a measure of confidence for each recognition result by indicating the range of uncertainty. By effectively addressing uncertainty and enhancing subject independence, the model can be integrated into real-life applications, improving interactions, learning, and accessibility for the hearing-impaired. Examples such as assistive devices, educational tools, and customer service automation highlight its transformative potential. The experimental evaluation demonstrates the superiority of the Type-2 Neutrosophic HMM over the Type-1 Neutrosophic HMM in terms of accuracy for SLR. Specifically, the Type-2 Neutrosophic HMM consistently outperforms its Type-1 counterpart across various test scenarios, achieving an average accuracy improvement of 10%. Full article

Accurate knowledge of the flow rate is essential for hydraulic systems, enabling the calculation of hydraulic power when combined with pressure measurements. These data are crucial for applications such as predictive maintenance. However, most flow rate sensors in fluid power systems operate invasively, disrupting the flow and producing inaccurate results, especially under transient conditions. Utilizing pressure transducers represents a non-invasive soft sensor approach since no physical flow rate sensor is used to determine the flow rate. Usually, this approach relies on the Hagen–Poiseuille (HP) law, which is limited to steady and incompressible flow. This paper introduces a novel soft sensor with an analytical model for transient, compressible pipe flow based on two pressure signals. The model is derived by solving fundamental fluid equations in the Laplace domain and converting them back to the time domain. Using the four-pole theorem, this model contains a relationship between the pressure difference and the flow rate. Several unsteady test cases are investigated and compared to a steady soft sensor based on the HP law, highlighting our soft sensor’s promising capability. It exhibits an overall error of less than $0.15$% for the investigated test cases in a distributed-parameter simulation, whereas the HP-based sensor shows errors in the double-digit range. Full article

The brewing industry is expanding with the rise of many small breweries. These are typically small and medium-sized enterprises producing a few hectoliters of beer per batch, often with limited investment capacity for equipment. Centrifugal pumps play a crucial role in microbreweries, facilitating the movement of wort throughout various stages of the brewing process. Failures in these systems, such as valve positioning issues or blockages, can lead to longer production times, increased energy consumption, and potential quality issues. This study explores a soft sensor approach for developing IFDs (Intelligent Fault Detection systems) by using pump drive data—current, torque, and power factor—without the need for additional sensors. Data were collected via a managed switch, and models were trained using Support Vector Machine and Multilayer Perceptron algorithms. The results indicate that this IFD method holds great potential for enhancing automation and maintenance in small breweries. Full article

This paper proposes a multifunctional soft robotic gripper for a Dobot robot to handle sensitive products. The gripper is based on pneumatic network (PneuNet) bending actuators. In this study, two different models of PneuNet actuators have been studied, designed, simulated, experimentally tested, and validated using two different techniques (3D printing and molding) and three different materials: FilaFlex 60A (3D-printed), Elastosil M4601, and Dragonskin Fast 10 silicones (with molds). A new soft gripper design for the Dobot robot is presented, and a new design/production approach with molds is proposed to obtain the gripper’s PneuNet multifunctional actuators. It also describes a new control approach that is used to control the PneuNet actuators and gripper function, using compressed air generated by a small compressor/air pump, a pressure sensor, a mini valve, etc., and executing on a low-cost controller board—Arduino UNO. This paper presents the main simulation and experimental results of this research study. Full article

The precise detection of effluent biological oxygen demand (BOD) is crucial for the stable operation of wastewater treatment plants (WWTPs). However, existing detection methods struggle to meet the evolving drainage standards and management requirements. To address this issue, this paper proposed a multivariable probability density-based auto-reconstruction bidirectional long short-term memory (MPDAR-Bi-LSTM) soft sensor for predicting effluent BOD, enhancing the prediction accuracy and efficiency. Firstly, the selection of appropriate auxiliary variables for soft-sensor modeling is determined through the calculation of k-nearest-neighbor mutual information (KNN-MI) values between the global process variables and effluent BOD. Subsequently, considering the existence of strong interactions among different reaction tanks, a Bi-LSTM neural network prediction model is constructed with historical data. Then, a multivariate probability density-based auto-reconstruction (MPDAR) strategy is developed for adaptive updating of the prediction model, thereby enhancing its robustness. Finally, the effectiveness of the proposed soft sensor is demonstrated through experiments using the dataset from Benchmark Simulation Model No.1 (BSM1). The experimental results indicate that the proposed soft sensor not only outperforms some traditional models in terms of prediction performance but also excels in avoiding ineffective model reconstructions in scenarios involving complex dynamic wastewater treatment conditions. Full article

The present study presents, for the first time, the real-time monitoring of an actual spiral-wound membrane unit used for ${\mathrm{CO}}_2$ removal from natural gas in an actual industrial offshore platform, utilizing a detailed phenomenological model. An Object-Oriented Programming (OOP) paradigm was employed to simulate the offshore membrane separation unit, accounting for the diverse levels of the membrane separation setup. A parameter estimation procedure was implemented to fit the phenomenological model to the real industrial data in real-time, for the first time. In addition, estimated permeance parameters and calculated unmeasured variables (soft sensor) were used for monitoring Key Performance Indicators (KPIs), such as membrane selectivity, dew point temperature, and hydrocarbon loss. Finally, a reparametrization of the parameters was implemented to improve the robustness of the optimization procedure. Thus, the model variables presented good adjustments to the data, indicating the satisfactory performance of the estimation. Consequently, the good accuracy of the model provided reliable information to the soft sensors and KPIs. Full article

Cardiac mapping is a crucial procedure for diagnosing and treating cardiac arrhythmias. Still, current clinical techniques face limitations including insufficient electrode coverage, poor conformability to complex heart chamber geometries, and high costs. This study explores the design, testing, and validation of a 64-electrode soft robotic catheter that addresses these challenges in cardiac mapping. A dual-layer flexible printed circuit board (PCB) was designed and integrated with sensors into a soft robotic sensor array (SRSA) assembly. Design considerations included flex PCB layout, routing, integration, conformity to heart chambers, sensor placement, and catheter durability. Rigorous SRSA in vitro testing evaluated the burst/leakage pressure, block force for electrode contact, mechanical integrity, and environmental resilience. For in vivo validation, a porcine model was used to demonstrate the successful deployment, conformability, and acquisition of electrograms in both the ventricles and atria. This catheter-deployable SRSA represents a meaningful step towards translating the integration of soft robotic actuators and stretchable electronics for clinical use, showcasing the unique mechanical and electrical performance that these designs enable. The high-density electrode array enabled rapid 2 s data acquisition with detailed spatial and temporal resolution, as illustrated by the clear and consistent cardiac signals recorded across all electrodes. The future of this work will lie in enabling high-density, anatomically conformable devices for detailed cardiac mapping to guide ablation therapy and other interventions. Full article

The agri-food sector is undergoing a comprehensive transformation as it transitions towards net zero. To achieve this, fundamental changes and innovations are required, including changes in how food is produced and delivered to customers, new technologies, data and physical infrastructures, and algorithmic advancements. In this paper, we explore the opportunities and challenges of deploying AI-based data infrastructures for sustainability in the agri-food sector by focusing on two case studies: soft-fruit production and brewery operations. We investigate the potential benefits of incorporating Internet of Things (IoT) sensors and AI technologies for improving the use of resources, reducing carbon footprints, and enhancing decision-making. We identify user engagement with new technologies as a key challenge, together with issues in data quality arising from environmental volatility, difficulties in generalising models, including those designed for carbon calculators, and socio-technical barriers to adoption. We highlight and advocate for user engagement, more granular availability of sensor, production, and emissions data, and more transparent carbon footprint calculations. Our proposed future directions include semantic data integration to enhance interoperability, the generation of synthetic data to overcome the lack of real-world farm data, and multi-objective optimisation systems to model the competing interests between yield and sustainability goals. In general, we argue that AI is not a silver bullet for net zero challenges in the agri-food industry, but at the same time, AI solutions, when appropriately designed and deployed, can be a useful tool when operating in synergy with other approaches. Full article

Robotic exoskeletons are being actively applied to support the activities of daily living (ADL) for patients with hand motion impairments. In terms of actuation, soft materials and sensors have opened new alternatives to conventional rigid body structures. In this arena, biomimetic soft systems play an important role in modeling and controlling human hand kinematics without the restrictions of rigid mechanical joints while having an entirely deformable body with limitless points of actuation. In this paper, we address the computational limitations of modeling large-scale articulated systems for soft robotic exoskeletons by integrating a parallel algorithm to compute the exoskeleton’s dynamics equations of motion (EoM), achieving a computation with $O\left(lo{g}_{2}n\right)$ complexity for the highly articulated n degrees of freedom (DoF) running on p processing cores. The proposed parallel algorithm achieves an exponential speedup for $n=p=64$ DoF while achieving a $0.96$ degree of parallelism for $n=p=256$, which demonstrates the required scalability for controlling highly articulated soft exoskeletons in real time. However, scalability will be bounded by the $n=p$ fraction. Full article

Recent advancements in biotechnology forged the path for clinical trials in dish (CTiDs) to advance as a popular method of experimentation in biomedicine. CTiDs play a fundamental role in translational research through technologies such as induced pluripotent stem cells, whole genome sequencing, and organs-on-a-chip. In this review, we explore advancements that enable these CTiD biotechnologies and their applications in animal testing, disease modeling, and space radiation technologies. Furthermore, this review dissects the advantages and disadvantages of CTiDs, as well as their regulatory considerations. Lastly, we evaluate the challenges that CTiDs pose and the role of CTiDs in future experimentation. Full article