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In rural low voltage grid networks, the use of battery in the households with a grid connected Photovoltaic (PV) system is a popular solution to shave the peak PV feed-in to the grid. For a single electricity price scenario, the existing forecast based control approaches together with a decision based control layer uses weather and load forecast data for the on–off schedule of the battery operation. These approaches do bring cost benefit from the battery usage. In this paper, the focus is to develop a Model Predictive Control (MPC) to maximize the use of the battery and shave the peaks in the PV feed-in and the load demand. The solution of the MPC allows to keep the PV feed-in and the grid consumption profile as low and as smooth as possible. The paper presents the mathematical formulation of the optimal control problem along with the cost benefit analysis . The MPC implementation scheme in the laboratory and experiment results have also been presented. The results show that the MPC is able to track the deviation in the weather forecast and operate the battery by solving the optimal control problem to handle this deviation.
A printed electronics technology has the advantage of additive and extremely low-cost fabrication compared with the conventional silicon technology. Specifically, printed electrolyte-gated field-effect transistors (EGFETs) are attractive for low-cost applications in the Internet-of-Things domain as they can operate at low supply voltages. In this paper, we propose an empirical dc model for EGFETs, which can describe the behavior of the EGFETs smoothly and accurately over all regimes. The proposed model, built by extending the Enz-Krummenacher-Vittoz model, can also be used to model process variations, which was not possible previously due to fixed parameters for near threshold regime. It offers a single model for all the operating regions of the transistors with only one equation for the drain current. Additionally, it models the transistors with a less number of parameters but higher accuracy compared with existing techniques. Measurement results from several fabricated EGFETs confirm that the proposed model can predict the I-V more accurately compared with the state-of-the-art models in all operating regions. Additionally, the measurements on the frequency of a fabricated ring oscillator are only 4.7% different from the simulation results based on the proposed model using values for the switching capacitances extracted from measurement data, which shows more than 2× improvement compared with the state-of-the-art model.
In this article, we present a taxonomy in Robot-Assisted Training; a growing body of research in Human–Robot Interaction which focuses on how robotic agents and devices can be used to enhance user’s performance during a cognitive or physical training task. Robot-Assisted Training systems have been successfully deployed to enhance the effects of a training session in various contexts, i.e., rehabilitation systems, educational environments, vocational settings, etc. The proposed taxonomy suggests a set of categories and parameters that can be used to characterize such systems, considering the current research trends and needs for the design, development and evaluation of Robot-Assisted Training systems. To this end, we review recent works and applications in Robot-Assisted Training systems, as well as related taxonomies in Human–Robot Interaction. The goal is to identify and discuss open challenges, highlighting the different aspects of a Robot-Assisted Training system, considering both robot perception and behavior control.
A Validated Quantification of Sudan Red Dyes in Spicery using TLC and a 16-bit Flatbed Scanner
(2018)
We present a video-densitometric quantification method for Sudan red dyes in spices and spice mixtures, separated by TLC. Application was done band-wise in small dots using a 5 μL glass pipette. For separation, the RP-18 plates (20 × 20 cm with fluorescent dye; Merck, Germany, 1.05559) were developed in a vertical developing chamber without vapor saturation from the starting point to a distance of 70 mm by using acetonitrile, methanol, and aqueous ammonia solution (25%; 8 + 1.8 + 0.2, v/v) as mobile phase. The quantification is based on direct measurements using an inexpensive 16-bit flatbed scanner for color measurements (in red, green, and blue). Evaluation of only the green channel makes the measurements very specific. For linearization, an extended Kubelka-Munk expression for data transformation was used. The range of linearity covers more than two magnitudes and lies between 20 and 500 ng. The extraction from a 2 g sample with acetonitrile, evaporation, and reconstitution to 200 μL with methanol and the band-wise application (7 mm) of a 10 μL sample allows a statistically defined LOD of less than 500 ppb of Sudan red dyes. To perform the analysis, a separation chamber, RP-18 plates, 5 μL glass pipettes, and a 16-bit flatbed scanner for 105 € are needed; therefore, the separation method is inexpensive, fast, and reliable.
Membrane distillation (MD) is a thermal separation process which possesses a hydrophobic, microporous
membrane as vapor space. A high potential application for MD is the concentration of hypersaline brines, such as
e.g. reverse osmosis retentate or other saline effluents to be concentrated to a near saturation level with a Zero
Liquid Discharge process chain. In order to further commercialize MD for these target applications, adapted MD
module designs are required along with strategies for the mitigation of membrane wetting phenomena. This
work presents the experimental results of pilot operation with an adapted Air Gap Membrane Distillation
(AGMD) module for hypersaline brine concentration within a range of 0–240 g NaCl /kg solution. Key performance
indicators such as flux, GOR and thermal efficiency are analyzed. A new strategy for wetting mitigation
by active draining of the air gap channel by low pressure air blowing is tested and analyzed. Only small reductions
in flux and GOR of 1.2% and 4.1% respectively, are caused by air sparging into the air gap channel.
Wetting phenomena are significantly reduced by avoiding stagnant distillate in the air gap making the air blower
a seemingly worth- while additional system component.
An Ultra-Low-Power RFID/NFC Frontend IC Using 0.18 μm CMOS Technology for Passive Tag Applications
(2018)
Battery-less passive sensor tags based on RFID or NFC technology have achieved much popularity in recent times. Passive tags are widely used for various applications like inventory control or in biotelemetry. In this paper, we present a new RFID/NFC frontend IC (integrated circuit) for 13.56 MHz passive tag applications. The design of the frontend IC is compatible with the standard ISO 15693/NFC 5. The paper discusses the analog design part in details with a brief overview of the digital interface and some of the critical measured parameters. A novel approach is adopted for the demodulator design, to demodulate the 10% ASK (amplitude shift keying) signal. The demodulator circuit consists of a comparator designed with a preset offset voltage. The comparator circuit design is discussed in detail. The power consumption of the bandgap reference circuit is used as the load for the envelope detection of the ASK modulated signal. The sub-threshold operation and low-supply-voltage are used extensively in the analog design—to keep the power consumption low. The IC was fabricated using 0.18 μm CMOS technology in a die area of 1.5 mm × 1.5 mm and an effective area of 0.7 mm2. The minimum supply voltage desired is 1.2 V, for which the total power consumption is 107 μW. The analog part of the design consumes only 36 μW, which is low in comparison to other contemporary passive tags ICs. Eventually, a passive tag is developed using the frontend IC, a microcontroller, a temperature and a pressure sensor. A smart NFC device is used to readout the sensor data from the tag employing an Android-based application software. The measurement results demonstrate the full passive operational capability. The IC is suitable for low-power and low-cost industrial or biomedical battery-less sensor applications. A figure-of-merit (FOM) is proposed in this paper which is taken as a reference for comparison with other related state-of-the-art researches.
In this paper the yield surface of a recently presented microstructure-based volume element of the gray cast iron material GJL-250 is assessed after different plastic loading histories. The evolution of the yield surface is investigated for different volumetric, deviatoric and uniaxial loadings. The micromechanical material properties of the metallic matrix and the graphite inclusions are validated by means experimental stress-strain hysteresis loops. The metallic matrix is modeled as elastic-plastic with a non-linear kinematic hardening law. The graphite inclusions are described by means of a volumetric strain state dependent Young’s modulus. The results show that the shape of the yield surface does not change significantly in comparison to the initial yield surface after pure deviatoric loadings. After volumetric loadings, the dependence of the material on the Lode angle is significantly reduced. Uniaxial tensile preloadings result in a deformed yield surface, whereby the magnitude of the deformation depends on the applied load. Uniaxial preloadings to compression do not change the shape of the initial yield surface.
Nowadays, robotic systems are an integral part of many orthopedic interventions. Stationary robots improve the accuracy but also require adapted surgical workflows. Handheld robotic devices (HHRDs), however, are easily integrated into existing workflows and represent a more economical solution. Their limited range of motion is compensated by the dexterity of the surgeon. This work presents control algorithms for HHRDs with multiple degrees of freedom (DOF). These algorithms protect pre- or intraoperatively defined regions from being penetrated by the end effector (e.g., a burr) by controlling the joints as well as the device’s power. Accuracy tests on a stationary prototype with three DOF show that the presented control algorithms produce results similar to those of stationary robots and much better results than conventional techniques. This work presents novel and innovative algorithms, which work robustly, accurately, and open up new opportunities for orthopedic interventions.
Pulmonary vein isolation (PVI) is a common therapy in atrial fibrillation (AF). The cryoballoon was invented to isolate the pulmonary vein in one step and in a shorter time than a point-by-point radiofrequency (RF) ablation. The aim of the study was to model two cryoballoon catheters, one RF catheter and to integrate them into a heart rhythm model for the static and dynamic simulation of PVI by cryoablation and RF ablation in AF. The modeling and simulation were carried out using the electromagnetic and thermal simulation software CST (CST, Darmstadt). Two cryoballons and one RF ablation catheter were modeled based on the technical manuals of the manufacturers Medtronic and Osypka. The PVI especially the isolation of the left inferior pulmonary vein using a cryoballoon catheter was performed with a -50 °C heatsource and an exponential signal. The temperature at the balloon surface was -50 °C after 20 s ablation time, -24 °C from the balloon 0,5 mm in the myocardium, at a distance of 1 mm -3 °C, at 2 mm 18 °C and at a distance of 3mm 29 °C. PVI with RF energy was simulated with an applied power of 5 W at 420 kHz at the distal 8 mm ablation electrode. The temperature at the tip electrode was 110 °C after 15 s ablation time, 75 °C from the balloon at 0,5 mm in the myocardium, at a distance of 1 mm 58 °C, at 2 mm 45 °C and at a distance of 3 mm 38 °C. Virtual heart rhythm and catheter models as well as the simulation of the temperature allow the simulation of PVI in AF by cryo ablation and RF ablation. The 3D simulation of the temperature profile may be used to optimize RF and cryo ablation.