Time series insights from the shopfloor: A real-world dataset of pneumatic pressure and electrical current in discrete manufacturing.

Gathered from a real-world discrete manufacturing floor, this dataset features measurements of pneumatic pressure and electrical current during production. Spanning 7 days and encompassing approximately 150 processed units, the data is organized into time series sampled at 100 Hz. The observed machine performs 24 steps to process each unit.

Halfway to Automated Feeding of Chinese Hamster Ovary Cells.

This paper presents a comprehensive study on the development of models and soft sensors required for the implementation of the automated bioreactor feeding of Chinese hamster ovary (CHO) cells using Raman spectroscopy and chemometric methods. This study integrates various methods, such as partial least squares regression and variable importance in projection and competitive adaptive reweighted sampling, and highlights their effectiveness in overcoming challenges such as high dimensionality, multicollinearity and outlier detection in Raman spectra. This paper emphasizes the importance of data preprocessing and the relationship between independent and dependent variables in model construction.

Stress Detection Using Frequency Spectrum Analysis of Wrist-Measured Electrodermal Activity.

The article deals with the detection of stress using the electrodermal activity (EDA) signal measured at the wrist. We present an approach for feature extraction from EDA. The approach uses frequency spectrum analysis in multiple frequency bands.

Improved Individualized Patient-Oriented Depth-of-Hypnosis Measurement Based on Bispectral Index.

Total intravenous anesthesia is an anesthesiologic technique where all substances are injected intravenously. The main task of the anesthesiologist is to assess the depth of anesthesia, or, more specifically, the depth of hypnosis (DoH), and accordingly adjust the dose of intravenous anesthetic agents. However, it is not possible to directly measure the anesthetic agent concentrations or the DoH, so the anesthesiologist must rely on various vital signs and EEG-based measurements, such as the bispectral (BIS) index.

Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments.

This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe different clusters, are estimated in a recursive manner. The main contribution of this work is the extension and application of the EPCC to 3D space for recursive and real-time detection of flat connected surfaces based on linear segments, which are all detected in an evolving way.

An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM.

Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data.

Automated vehicle driveaway with a manual dry clutch on chassis dynamometers: Efficient identification and decoupling control.

The testing and certification of vehicles in succession to their development are usually achieved on chassis dynamometer testbeds. In this context, the execution of test cycles shall be performed as automatised as possible even if vehicles with manual transmission powertrain are under test. A particular challenge is the vehicle driveaway from standstill by actuating clutch and engine in accordance with each other.

Modular Serial Flow Through device for pulsed electric field treatment of the liquid samples.

In biotechnology, medicine, and food processing, simple and reliable methods for cell membrane permeabilization are required for drug/gene delivery into the cells or for the inactivation of undesired microorganisms. Pulsed electric field treatment is among the most promising methods enabling both aims. The drawback in current technology is controllable large volume operation.

Embedded Control System for Smart Walking Assistance Device.

This paper presents the design and implementation of a unique control system for a smart hoist, a therapeutic device that is used in rehabilitation of walking. The control system features a unique human-machine interface that allows the human to intuitively control the system just by moving or rotating its body. The paper contains an overview of the complete system, including the design and implementation of custom sensors, dc servo motor controllers, communication interfaces and embedded-system based central control system.

The Detection Rate of Enteric Viruses and Clostridium difficile in a Waste Water Treatment Plant Effluent.

Waste water treatment plant (WWTP) is considered as an important source of surface water contamination by enteric pathogens. In this study, we describe the occurrence of enteric viruses (group A rotaviruses, noroviruses, astroviruses, sapoviruses, hepatitis A virus, and hepatitis E virus) and Clostridium difficile in the effluent of a wastewater treatment plant during a 1-year period. Enteric viruses were simultaneously and efficiently concentrated in a single step using methacrylate monolithic chromatographic support.

Nonlinear system identification by Gustafson-Kessel fuzzy clustering and supervised local model network learning for the drug absorption spectra process.

This paper deals with the problem of fuzzy nonlinear model identification in the framework of a local model network (LMN). A new iterative identification approach is proposed, where supervised and unsupervised learning are combined to optimize the structure of the LMN. For the purpose of fitting the cluster-centers to the process nonlinearity, the Gustafsson-Kessel (GK) fuzzy clustering, i.

Recursive fuzzy c-means clustering for recursive fuzzy identification of time-varying processes.

In this paper we propose a new approach to on-line Takagi-Sugeno fuzzy model identification. It combines a recursive fuzzy c-means algorithm and recursive least squares. First the method is derived and than it is tested and compared on a benchmark problem of the Mackey-Glass time series with other established on-line identification methods.

Tableting process optimisation with the application of fuzzy models.

A quality-by-design (QbD) principle, including process analytical technology, is becoming the principal idea in drug development and manufacturing. The implementation of QbD into product development and manufacturing requires larger resources, both human and financial, however, large-scale production can be established in a more cost-effective manner and with improved product quality. The objective of the present work was to study the influence of particle size distribution in powder mixture for tableting, and the settings of the compression parameters on the tablet quality described by the capping coefficient, standard deviations of mass and crushing strength of compressed tablets.

Using a LRF sensor in the Kalman-filtering-based localization of a mobile robot.

This paper deals with the problem of estimating the output-noise covariance matrix that is involved in the localization of a mobile robot. The extended Kalman filter (EKF) is used to localize the mobile robot with a laser range finder (LRF) sensor in an environment described with line segments. The covariances of the observed environment lines, which compose the output-noise covariance matrix in the correction step of the EKF, are the result of the noise arising from a range-sensor's (e.

Minimisation of the capping tendency by tableting process optimisation with the application of artificial neural networks and fuzzy models.

The pharmaceutical industry is increasingly aware of the advantages of implementing a quality-by-design (QbD) principle, including process analytical technology, in drug development and manufacturing. Although the implementation of QbD into product development and manufacturing inevitably requires larger resources, both human and financial, large-scale production can be established in a more cost-effective manner and with improved efficiency and product quality. The objective of the present work was to study the influence of particle size (and indirectly, the influence of dry granulation process) and the settings of the tableting parameters on the tablet capping tendency.

Fuzzy-model-based hybrid predictive control.

In this paper we present a method of hybrid predictive control (HPC) based on a fuzzy model. The identification methodology for a nonlinear system with discrete state-space variables based on combining fuzzy clustering and principal component analysis is proposed. The fuzzy model is used for HPC design, where the optimization problem is solved by the use of genetic algorithms (GAs).

Identification of the phase code in an EEG during gripping-force tasks: a possible alternative approach to the development of the brain-computer interfaces.

Background: The subject of brain-computer interfaces (BCIs) represents a vast and still mainly undiscovered land, but perhaps the most interesting part of BCIs is trying to understand the information exchange and coding in the brain itself. According to some recent reports, the phase characteristics of the signals play an important role in the information transfer and coding. The mechanism of phase shifts, regarding the information processing, is also known as the phase coding of information.

Gripping-force identification using EEG and phase-demodulation approach.

In this paper we investigate the fuzzy identification of brain-code during simple gripping-force control tasks. Since the synchronized oscillatory activity and the phase dynamics between the brain areas are two important mechanisms in the brain's function and information transfer, we decided to examine whether it is possible to extract the encoded information from the EEG signals using the phase-demodulation approach. The EEG was measured during the performance of different visuomotor tasks and the information we were trying to decode was the gripping force as applied by the subjects.

Interval fuzzy modeling applied to Wiener models with uncertainties.

This correspondence addresses the problem of interval fuzzy model identification and its use in the case of the robust Wiener model. The method combines a fuzzy identification methodology with some ideas from linear programming theory. On a finite set of measured data, an optimality criterion which minimizes the maximum estimation error between the data and the proposed fuzzy model output is used.