Sensor Inputs Research Articles

Measuring the magnetic field of a low frequency LC-circuit with phyphox

In high school, LC(R)-circuits are an integral component of physics education. We propose a new setup for exploring those circuits utilizing the magnetic field sensor of a smartphone in combination with the application phyphox, which reads and displays sensor input. Coils with high inductance and low resistance generate low frequency oscillations which can be visualized without any measuring devices other than a smartphone. The results are very satisfactory, because the resolution of the graph is high and the theoretical values match principal features of the measurements. Also, the setup can be achieved with low-cost components.

Performance analysis of coloured BIPV systems depending on reliability of irradiance data

In recent times, there has been a global increase in the building integrated photovoltaic (BIPV) system market. In addition, the capacity for aesthetic purposed coloured BIPV system integration has also increased. The performance of the coloured BIPV system is measured with Plane of Array (PoA) irradiance using the expected output power as a variable. In this paper, the output power was calculated based on the installation conditions of the sensor and the configuration of the BIPV system. The expected power of the PV system was calculated using the PV performance analysis program PVsyst, a simulation tool. To analyze the performance ratio relative to the irradiance data, the simulation results were compared with the monitoring data. The mean bias error (MBE) between the simulated data and the measured output power of the BIPV system was determined based on the installation condition of the input sensor and configuration of the BIPV system. The MBE values of output power dependent on sensor reliability showed a maximum of −19.44 % and a minimum of −1.4 %. Also, the calculated performance of the coloured BIPV system was 67.7 %, 71.6 %, and 67.7 %, respectively.

Simultaneous actuator and sensor fault reconstruction of singular delayed linear parameter varying systems in the presence of unknown time varying delays and inexact parameters

SummaryIn this article, robust fault diagnosis of a class of singular delayed linear parameter varying systems is considered. The considered system has delayed dynamics with unknown time varying delays and also it is affected by noise, disturbance and faults in both actuators and sensors. Moreover, in addition to the aforementioned unknown inputs and uncertainty, another source of uncertainty related to inexact measures of the scheduling parameters is present in the system. Making use of the descriptor system approach, sensor faults in the system are added as additional states into the original state vector to obtain an augmented system. Then, by designing a suitable proportional double integral unknown input observer (PDIUIO), the states, actuator, and sensor faults are estimated. The uncertainty due to the mismatch between the inexact parameters that schedule the observer and the real parameters that schedule the original system is formulated with an uncertain system approach. In the PDIUIO, the uncertainty induced by unknown inputs (disturbance, noise and actuator, and sensor faults), unknown delays, and inexact parameter measures are attenuated in H∞ sense with different weights. The constraints regarding the existence and the robust stability of the designed PDIUIO are formulated using linear matrix inequalities. The efficiency of the proposed method is verified using an application example based on an electrical circuit.

Modelling Fuel Cut Off Controller on CNG Engines Using Fuzzy Logic: A Prototype

Compressed Natural Gas (CNG) is an alternative solution to the limited availability of fossil energy. CNG use's advantages include high octane value, applicable to vehicles requiring large power, cost-effectiveness, and lower emissions. However, applying the old CNG kit leaves emission problems and fuel wastage during deceleration. Although numerous studies have been carried out with numerous variables to improve engine performance and emissions, reports regarding deceleration interventions are still limited. Therefore, this study proposes enhanced modeling to optimize the fuel cut-off controller by applying fuzzy logic by controlling the throttle valve position based on the input sensor of the engine. Engine dynamics, fuel characteristics, and intake systems are considered strictly in the development of the control system to obtain more precise results that refer to the complete combustion process. The designed model has advantages over previous studies, which focused on achieving CNG AFR stoichiometry to improve fuel economy by using the fuel cut-off method during deceleration. The results showed that fuel savings could be increased during deceleration by cutting off fuel flow to the engine. This can be seen from the increase in AFR ± 57% and decrease ± 38% - 67% in CNG flow rate during deceleration which is promising to be widely applied. In the future, the proposed model could be used as part of the vehicle component in optimizing the fuel consumption that will support green technology sustainability.

Secure Encoded Instruction Graphs for End-to-End Data Validation in Autonomous Robots

As autonomous robots are becoming more widespread, more attention is being paid to the security of robotic operation. Autonomous robots can be seen as cyber-physical systems: they can operate in virtual, physical, and human realms. Therefore, securing the operations of autonomous robots requires not only securing their data (e.g., sensor inputs and mission instructions) but securing their interactions with their environment. There is currently a deficiency of methods that would allow robots to securely ensure their sensors and actuators are operating correctly without external feedback. This paper introduces an encoding method and end-to-end validation framework for the missions of autonomous robots. In particular, we present a proof of concept of a map encoding method, which allows robots to navigate realistic environments and validate operational instructions with almost zero {\it a priori} knowledge. We demonstrate our framework using two different encoded maps in experiments with simulated and real robots. Our encoded maps have the same advantages as typical landmark-based navigation, but with the added benefit of cryptographic hashes that enable end-to-end information validation. Our method is applicable to any aspect of robotic operation in which there is a predefined set of actions or instructions given to the robot.

Design and Implementation of Intelligent Traffic Control System using Programmable Logic Controller

Generally, traffic light systems are regarded as open loop control systems, that is, they are in continuous operation without any form of feedback that can help the system take a decision based on certain conditions. And so, this is specifically part of the reason why there exist high vehicular densities at roundabouts. Having considered in providing a solution to this problem, this project integrated a 4I/O Programmable Logic Controller (PLC) into the traffic light system circuitry which provided feedback based on signals received from the input sensors connected to the PLC. When these sensors get energized due to the presence of a vehicle, it sends an ON-bit signal to the PLC through the input port terminating the sensor and the PLC. This signal in turn triggers the output port mapped in the microprocessor so that it energizes the Light Emitting Diodes (LEDs). During simulation, two forms of control were used: Primary (default) and Secondary control systems. On the secondary control, the PLC receives signal from the ultrasonic sensor on road tracks A and B then allows long queued vehicles to pass while it stops vehicular movement on road track C and D and vice versa. It is identified that this method of traffic light control has helped to ensure orderliness and evenly distributed vehicular flow at case study roundabout.

Toward Rapid and Automated Immunoassays: Using a Localized Electrochemical pH Modulation Platform to Perform a Single-Step Immunoassay.

An electrochemical platform for generating and controlling a localized pH microenvironment on demand is proposed by employing a closed-loop control algorithm based on an iridium oxide pH sensor input. We use a combination of solution-borne quinones and galvanostatic excitation on a prepatterned indium tin oxide (ITO) working electrode to modulate pH within a very well confined, small volume of solution close to the electrode surface. We demonstrate that the rate of pH change can be controlled at up to 2 pH s-1 with an excellent repeatability (±0.004). The desired pH microenvironment can be stably maintained for longer than 2 h within ±0.0012 pH. As a high-impact application of the platform technology, we propose a single-step immunoassay and demonstrate its utility in measuring C-reactive protein (CRP), a critical inflammatory marker in various conditions such as myocardial infarction and even SARS-Cov-2. Utilizing pH modulation technology along with pH-sensitive fluorescence dye simplifies the immunoassay process into a single-step, where a mixture of all of the reagents is incubated only for 1 h without any washing steps or the need to change solution. This simplified immunoassay process minimizes the hands-on time of the end-user and thus decreases technician-driven errors. Moreover, the absence of complicated liquid-handling hardware makes it more suitable and attractive for an ultracompact platform to ultimately be used in a point-of-care diagnostic assay.

Forecasting thermoacoustic instabilities in liquid propellant rocket engines using multimodal Bayesian deep learning

We present a method that combines multiple sensory modalities in a rocket thrust chamber to predict impending thermoacoustic instabilities with uncertainties. This is accomplished by training an autoregressive Bayesian neural network model that forecasts the future amplitude of the dynamic pressure time series, using multiple sensor measurements (injector pressure/ temperature measurements, static chamber pressure, high-frequency dynamic pressure measurements, high-frequency OH* chemiluminescence measurements) and future flow rate control signals as input. The method is validated using experimental data from a representative cryogenic research thrust chamber. The Bayesian nature of our algorithms allows us to work with a dataset whose size is restricted by the expense of each experimental run, without making overconfident extrapolations. We find that the networks are able to accurately forecast the evolution of the pressure amplitude and anticipate instability events on unseen experimental runs 500 milliseconds in advance. We compare the predictive accuracy of multiple models using different combinations of sensor inputs. We find that the high-frequency dynamic pressure signal is particularly informative. We also use the technique of integrated gradients to interpret the influence of different sensor inputs on the model prediction. The negative log-likelihood of data points in the test dataset indicates that prediction uncertainties are well-characterized by our model and simulating a sensor failure event results in a dramatic increase in the epistemic component of the uncertainty, as would be expected when a Bayesian method encounters unfamiliar, out-of-distribution inputs.

Study on the Application of Microcontroller-Based GPS Sensors on Floater Gliders to Reach the Specified Destination

The purpose of the study is to determine the performance of the control system of the floater glider to a predetermined location. The method used was a combination between software simulation and an experimental free-running test. The Floater control system is equipped with GPS as input and output sensors to determine coordinates or waypoints. The microcontroller mounted was Pixhawk 2.4.8 based on ARM cortex M4 – 32 bits. The floater was driven by two Brushless DC motors and servo motors to drive the Z-Peller steering to the desired trajectory. The floater system applies the default application software from Pixhawk, namely mission planner to plan and also monitor the actual trajectory. To have a proper procedure for testing the designed control system, the three stages of the test were conducted, firstly numerical tuning test using step function on MATLAB simulation, then trajectory simulation test of waypoint using mission planner, and finally the field test in the ILH pond. From the three stages of the test, it was found that the selection of PID gain values Kp = 0.2, Ki = 0.3, and Kd = 0.02, indicating the performance of the floater glider control system to the planned location has a good fit between the trajectory simulation results and the waypoint testing in the pond. These results can be used as a reference in conducting further research so that the navigation accuracy of the control system will be improved.

Towards Real-Time Minimum-Input Prediction of Lumbar Moment Based on Flexible Sensors and Machine Learning

Knowledge of low-back loading is essential for understanding and mitigating the risk of low-back overexertion injuries. Conventional data acquisition methods for estimating joint loading are limited to laboratory settings, whereas wearable sensors can provide a mobile and cost-effective alternative. This study investigated the feasibility of learning prediction of L5S1 flexion moment based on kinematics and electromyography (EMG) measurements from flexible sensors. Four machine learning methods were compared, and different subsets of sensor inputs were explored. Results indicated that the support vector machine (SVM) method outperformed others, and a subset of four out of seven sensor locations, namely sacrum, thigh, shank, and thoracic erector spinae, yielded the best predictive accuracy. The study demonstrates that machine learning can unlock the potential of mobile miniaturized flexible sensors in field biomechanics or ergonomics studies.

A 96.9-dB-Resolution 109-μW Second-Order Robust Closed-Loop VCO-Based Sensor Interface for Multiplexed Single-Ended Resistance Readout in 180-nm CMOS

This article presents a highly digital robust voltage-controlled oscillator (VCO)-based front end for multiplexed single-ended resistive sensor readout applications. The architecture features a modified digital phase-locked loop (DPLL) structure that enables second-order noise shaping without any operational transconductance amplifiers (OTAs) and a single feedback digital-to-analog converter (DAC). The direct conversion of the sensor input resistance to time-domain information obviates the need for any conditioning circuit, thus resulting in a highly digital and area-compact architecture. The closed loop substantially reduces the amplitude of the signal at the VCO input, thereby achieving high linearity. To ensure high robustness against supply and temperature variations, a double measurement approach is employed. Fabricated in a 180-nm CMOS process with a 0.1-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> active area, the sensor readout consumes 109 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> of power and achieves a resolution of 96.9 dB for 0.5-ms conversion time, resulting in a Schreier figure of merit (FoM) of 166.5 dB. The circuit only requires a cheap and efficient single-point trimming calibration at room temperature. The low conversion time enables multiplexing among multiple sensor readouts. The supply and temperature sensitivities are 0.4%/V and 36 ppm/°C, respectively.

Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning.

Deep neural network-based systems are now state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness. Small perturbations to sensor inputs (from noise or adversarial examples) are often enough to change network-based decisions, which was recently shown to cause an autonomous vehicle to swerve into another lane. In light of these dangers, numerous algorithms have been developed as defensive mechanisms from these adversarial inputs, some of which provide formal robustness guarantees or certificates. This work leverages research on certified adversarial robustness to develop an online certifiably robust for deep reinforcement learning algorithms. The proposed defense computes guaranteed lower bounds on state-action values during execution to identify and choose a robust action under a worst case deviation in input space due to possible adversaries or noise. Moreover, the resulting policy comes with a certificate of solution quality, even though the true state and optimal action are unknown to the certifier due to the perturbations. The approach is demonstrated on a deep Q-network (DQN) policy and is shown to increase robustness to noise and adversaries in pedestrian collision avoidance scenarios, a classic control task, and Atari Pong. This article extends our prior work with new performance guarantees, extensions to other reinforcement learning algorithms, expanded results aggregated across more scenarios, an extension into scenarios with adversarial behavior, comparisons with a more computationally expensive method, and visualizations that provide intuition about the robustness algorithm.

Observers for Differential Algebraic Equation Models of Power Networks: Jointly Estimating Dynamic and Algebraic States

Phasor measurement units (PMUs) have become instrumental in modern power systems for enabling real time, wide-area monitoring and control. Accordingly, many studies have investigated efficient and robust dynamic state estimation (DSE) methods in order to accurately compute the dynamic states of generation units. Nonetheless, most of them forego the dynamic-algebraic nature of power networks and only consider their nonlinear dynamic representations. Motivated by the lack of DSE methods based on power network’s differential-algebraic equations (DAEs), this article develops a novel observer-based DSE framework in order to perform simultaneous estimation of the dynamic and algebraic states of multimachine power networks. Specifically, we leverage the DAE dynamics of a power network around an operating point and combine them with a PMU-based measurement model capable of capturing bus voltages and line currents. The proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> observer, which only requires detectability and impulse observability conditions that are satisfied for various power networks, is designed to handle various noise, unknown inputs, and input sensor failures. The results obtained from performing extensive numerical simulations on IEEE 9-bus and 39-bus systems showcase the effectiveness of the proposed approach for DSE purposes.

Automatic Determination of Rock-Breaking Target Poses for Impact Hammers

This paper describes a system for the automatic determination of rock-breaking target poses for impact hammers used in underground mines. The rock-breaking target pose is defined as the position and angle at which the impact hammer must strike a rock in order to break it. The automatic determination of this pose is essential for the autonomous operation of an impact hammer. The proposed system takes as input sensor data composed of point clouds and images, and automatically determines a rock-breaking target pose. The system consists of a rock segmentation subsystem that receives the sensor data and identifies and individualizes the rocks/boulders present above the grizzly, and a rock-breaking target pose generation and evaluation subsystem that receives the rock information produced by the rock segmentation subsystem, and generates a list of rock-breaking target pose candidates, it evaluates them, and it selects the best candidate as the rock-breaking target pose. The system is evaluated using real data. The reported experiments show the system’s capability to generate appropriate target poses.

Rancang Bangun Alat Ukur Tribometer Pin-On-Disc Dengan Penggerak Motor Dc Gearbox 12 Volt

Tribometer is a tool to determine the occurrence of surface contact wear. The purpose of this research is to design a pin-on-disc tribometer. The design stage involves making a mechanical design, a series of sensors, and designing a complete tool. Tribometer input and output electrical sensor circuit. The software used to program the tribometer is Arduino and uses C++ programming. The pin on disc test stages includes weighing the disc and load variations and setting the disc rotation as desired.

Machine-Learning-Based Reconstruction of Turbulent Vortices From Sparse Pressure Sensors in a Pump Sump

Abstract Getting access to the state of turbulent flow from limited sensor measurements in engineering systems is a major challenge. Development of technologies to accurately estimate the state of the flow is now possible with the use of machine learning. We present a supervised machine learning technique to reconstruct turbulent vortical structures in a pump sump from sparse surface pressure measurements. For the current flow reconstruction technique, a combination of multilayer perceptron and three-dimensional convolutional neural network is utilized. This technique provides accurate flow estimation from only a few sensor measurements, identifying the presence of adverse vortices. The dependence of the model performance on the amount of training data, the number of input sensors, and the noise levels are investigated. The present machine learning-based flow estimator supports safe operations of pumps and can be extended to a broad range of applications for industrial fluid-based systems.

A secure IoT based healthcare framework using modified RSA algorithm using an artificial hummingbird based CNN

AbstractRecently, a wide variety of real‐world problems are solved via cloud computing, deep learning, machine learning, artificial intelligence, and the Internet of Things (IoT). These methodologies are concerned with different areas like smart cities, agriculture, transportation systems, and healthcare systems. The existing researchers focused on the health care monitoring application along with IoT and cloud computing. It met several shortcomings in case of computational complexity, cost, time, and improper health care data storage and so forth. To overcome these challenges, the novel IoT‐enabled secure healthcare monitoring model is proposed in this study. At first, several sensors are deployed in the human body thereby collecting patients' data with respect to vital parameters like body temperature deviation. The patient's health record dataset consists of 10 attributes namely phone numbers, marital status, address, age, name, heartbeat rate, oxygen level, smoking, temperature, and blood pressure. The data size reduction and normalization are performed in the IoT medical sensor dataset which contains the redundant or irrelevant attributes that are eliminated during pre‐processing. The artificial hummingbird (AHB) algorithm‐based convolutional neural network (AHB‐CNN) model performs both feature extraction and classification of cancer disease. The AHB‐CNN model classifies whether the patient is prone to cancer or not based on the sensor input collected. The received results are then sent to the hospital management for analysis. The Rivest‐Shamir‐Adleman (RSA) encryption method is mostly used in this study due to the major benefits it gives in terms of asymmetric encryption, ease of use, simpler deployment, and high security associated with factoring large prime numbers. A modified RSA algorithm is used in this article which uses the double encryption‐decryption process and “n” prime numbers to enhance the security of the conventional RSA algorithm. The data is always encrypted during transit before being stored in the cloud or for further processing. Depending upon the experimental consequences, the proposed method established superior performances compared to other state‐of‐art techniques.

PROTOTIPE ALAT SISTEM SORTIR DIMENSI, BERAT DAN BARCODE KOTA TUJUAN BERBASIS MIKROKONTROLER ARDUINO MEGA 256O

ABSTRACT&#x0D; &#x0D; This prototype is a control system for sorting package based on dimensions, weight and the barcode of the destination city of the package. In this prototype, it has Load Cell sensor input as weight input in sorting, Barcode Reader for reading destination city input on the barcode, 2 Hc-Sr04 sensors as item dimension input and infrared sensor as item detection input. As for the output, namely 11 servo motors as actuators, and LCD. This tool works by sorting package into 4 areas of the destination city by reading barcode data for each item and 2 boxes in each area of the destination city, namely motorbike boxes for small items, and car boxes for large items. Package are declared large if the length, width, height, and weight exceed the specified kkm value, which is 10 cm with a tolerance of 1 cm and weight &gt; 100 grams. From the sorting testing process, this tool yielded 93.34% success. Although there are still technical problems with the guider when positioning the goods.&#x0D; &#x0D; Keywords : Sorter, Ultrasonic, Barcode scanner, Load cell, Conveyor, Arduino Mega 2560

Improved Active Disturbance Rejection Control (ADRC) with Extended State Filters

To address time delay and noise problems in control systems, in this study, we integrated an extended state filter for signal filtering into an active disturbance rejection control (ADRC) system and derived an improved ADRC approach. In addition to the active anti-disturbance and active tracking estimation functions of the existing ADRC, the proposed approach also includes active filtering and active advance prediction functions, which can filter out the effect of measurement noise on system state observation while reducing the delay between the system control output and the detection of the sensor input. We verified through an evaluation in a simulation environment that the proposed approach may be expected to achieve improved control accuracy and increase the stability of closed-loop control systems.

PROTOTIPE ALAT SISTEM SORTIR DIMENSI, BERAT DAN BARCODE KOTA TUJUAN BERBASIS MIKROKONTROLER ARDUINO MEGA 256O

ABSTRACT&#x0D; &#x0D; This prototype is a control system for sorting package based on dimensions, weight and the barcode of the destination city of the package. In this prototype, it has Load Cell sensor input as weight input in sorting, Barcode Reader for reading destination city input on the barcode, 2 Hc-Sr04 sensors as item dimension input and infrared sensor as item detection input. As for the output, namely 11 servo motors as actuators, and LCD. This tool works by sorting package into 4 areas of the destination city by reading barcode data for each item and 2 boxes in each area of the destination city, namely motorbike boxes for small items, and car boxes for large items. Package are declared large if the length, width, height, and weight exceed the specified kkm value, which is 10 cm with a tolerance of 1 cm and weight &gt; 100 grams. From the sorting testing process, this tool yielded 93.34% success. Although there are still technical problems with the guider when positioning the goods.&#x0D; &#x0D; Keywords : Sorter, Ultrasonic, Barcode scanner, Load cell, Conveyor, Arduino Mega 2560