Instantaneous Detection Research Articles

Secure Control of DC Microgrids for Instant Detection and Mitigation of Cyber-Attacks Based on Artificial Intelligence

DC microgrids can be operated under a hierarchical control strategy, and it needs a communication-based layer. The implementation of digital controllers and the communication infrastructure can make a dc microgrid vulnerable to cyber-attacks. This article introduces an approach based on Artificial Intelligence (AI) to detect and mitigate cyber-attacks in a dc microgrid. The proposed method is based on the artificial neural network (ANN), which can be categorized as an AI-based method. The proposed application implements an ANN to detect and mitigate false data injection attacks (FDIAs). FDIAs try to inject false data into the system to affect the control application of the dc microgrid, and it can shut down the dc microgrid. The proposed method can calculate the value of the false data, and it can detect and remove the attack simultaneously. The proposed method is tested in a MATLAB/Simulink environment. Also, to have more accurate results, the introduced approach is examined under different conditions and cyber/physical disturbances (e.g., communication delay, noise, plug-and-play of additional units, and time-varying FDIAs). Besides, a comparison is considered to evaluate the effectiveness of the proposed strategy. The obtained results can conclusively prove the effectiveness, accuracy, and authenticity of the proposed method to successfully detect the FDIAs and remove the cyber-attack.

Estimation radius regression in PLIC-VOF method for droplet evaporation

Heat & fluid flow conservation equations of carburant liquid droplet are solved by a semi-implicit method of the Volume Of Fluid ( VOF ) scheme. A new technique of the droplet radius regression estimation based on the Piece-wise Linear Interface Calculation ( PLIC ) is developed. Numerical computation is performed for stagnant carburant droplets in evaporation in natural convection (which takes into consideration the buoyancy forces) and in an air atmosphere at a pressure of 1 bar. The results are presented in the form of radius, area and mass regressions, volumetric mass flow, liquid temperature, surface temperature, and vapor temperature evolutions by taking into account the influence of the ambient temperature, the initial radius, and the component variations. Using the time evolution of the color function, our radius regression estimation technique is related to the instant detection of the liquid volume fraction. This regression is not affected by the changing of the droplet’s shape nor the temperature collecting and synchronization with the method for the evaporation analysis.

A Combined Semi-Supervised Deep Learning Method for Oil Leak Detection in Pipelines Using IIoT at the Edge

Pipelines are integral components for storing and transporting liquid and gaseous petroleum products. Despite being durable structures, ruptures can still occur, resulting not only in financial losses and energy waste but, most importantly, in immeasurable environmental disasters and possibly in human casualties. The objective of the ESTHISIS project is the development of a low-cost and efficient wireless sensor system for the instantaneous detection of leaks in metallic pipeline networks transporting liquid and gaseous petroleum products in a noisy industrial environment. The implemented methodology is based on processing the spectrum of vibration signals appearing in the pipeline walls due to a leakage effect and aims to minimize interference in the piping system. It is intended to use low frequencies to detect and characterize leakage to increase the range of sensors and thus reduce cost. In the current work, the smart sensor system developed for signal acquisition and data analysis is briefly described. For this matter, two leakage detection methodologies are implemented. A 2D-Convolutional Neural Network (CNN) model undertakes supervised classification in spectrograms extracted by the signals acquired by the accelerometers mounted on the pipeline wall. This approach allows us to supplant large-signal datasets with a more memory-efficient alternative to storing static images. Second, Long Short-Term Memory Autoencoders (LSTM AE) are employed, receiving signals from the accelerometers, and providing an unsupervised leakage detection solution.

A fiber optic conjugate stress sensor for instantaneous tangent modulus detection targeting prognostic health monitoring applications

In this work, a surface-mounted fiber optic conjugate stress sensor (FOCSS) is developed, fabricated, and experimentally demonstrated to accurately evaluate the instantaneous tangent modulus of various metals, thus allowing measurement of the elastic-plastic stress–strain behavior of the host material. The originally proposed embedded version of the conjugate stress (CS) sensor measured the deformations experienced in two fully embedded, ellipsoidal, collocated sensors of significantly different stiffnesses, and related those sensor deformations to host modulus via Eshelby’s equivalent inclusion method. In contrast, the current experimental study focuses on a surface-mounted implementation of the CS sensor. As Eshelby’s analysis is not applicable in this case, a simplified lumped-element spring model is developed to instantaneously relate the deformations in the CS sensor to the local stress–strain state of the host. Therefore, tangent modulus of the host can be estimated from the output of the FOCSS, which can serve as an important damage precursor in structural and prognostic health monitoring for a prominent set of failure mechanisms, including mechanical overstress, fatigue, and corrosion. In this study, the FOCSS data is used in conjunction with the lumped-element spring model to measure the elastic modulus of three different materials of significantly different stiffness: aluminum, copper, and steel. Additionally, as validation of instantaneous tangent modulus tracking, the elastic-plastic stress–strain curves of copper and steel are reconstructed from the FOCSS outputs during uniaxial tensile tests (with a goodness of fit R 2 > 0.98). The results demonstrate the ability of the FOCSS to detect instantaneous modulus as materials experience plastic deformation.

Cyanide Anion Determination Based on Nucleophilic Addition to 6-[(E)-(4-Nitrophenyl)diazenyl]-1′,3,3′,4-tetrahydrospiro[chromene-2,2′-indole] Derivatives

This work provides a novel approach for the instant detection of CN− anions based on chromogenic 6-[(E)-(4-nitrophenyl)diazenyl]-1′,3,3′,4-tetrahydrospiro[chromene-2,2′-indole] derivatives. New colorimetric detectors were synthesized and characterized. These compounds exhibited a substantial color change from orange to magenta and blue when treated with cyanide ions in a CH3CN solution buffered with sodium phosphate and demonstrated high selectivity to CN− anions. Common anions were tested, and they did not interfere with cyanide detection, except for carbonates and hydrosulfites. The simple preparation of a molecular sensor and the easily noticeable color change makes this a practical system for the monitoring of CN− ions. This color change is explained by nucleophilic substitution of the pyrane ring oxygen atom at the indoline C-2 atom by the cyanide anion. This generates the appearance of intensively colored 4-(4-nitrophenylazo)phenolate chromophore and allows for determining very low levels of CN− anion.

Metal‐Organic Frameworks: Synthesis, Structures, and Applications

Metal‐Organic Frameworks: Synthesis, Structures, and Applications

Application of instantaneous phase detection technology based on laser displacement sensor in fault diagnosis of spur gear of rotation vector reducer

Aiming at the problem of a spur gear fault of the rotation vector (RV) reducer in industrial robots, an instantaneous phase detection technology based on eccentric modulation and laser displacement sensor is proposed. Taking the RV-20E reducer as the object, through the analysis of the RV reducer gear fault mechanism, an RV reducer fault diagnosis test bed is developed to simulate the actual operating conditions of the RV reducer of industrial robot. The instantaneous phase information of the RV reducer is collected, and the adaptive permutation entropy is used to process the instantaneous phase data, which can judge the gear fault type quickly and effectively. The test results show that the fault diagnosis test bench based on this detection technology can effectively and quantitatively judge the fault type of the spur gear of the RV reducer.

A Multicenter Metal–Organic Framework for Quantitative Detection of Multicomponent Organic Mixtures

A Multicenter Metal–Organic Framework for Quantitative Detection of Multicomponent Organic Mixtures

Instantaneous harmonic detection based on composite observer for distort grid with DC bias and variable frequency

This paper presents a real-time detection method for harmonic extraction, which can deal with sudden fluctuation of amplitude, phase, and frequency of the fundamental wave, and has advantages of rapid speed and high accuracy. First, using the Least Squares method with forgetting factor to extract phase and frequency of the fundamental wave, then the phase information is used to design a linear time-varying observer, which is based on a state space model constructed from the grid signal and harmonic components, to estimate state variables and realize the extraction of DC bias and harmonics. Taking Lyapunov function and Lasalle invariant set principle to prove stability and convergence of the proposed observer. Additionally, the input observer method and the definition method are given for DC bias. Simulation and experimental results show that even if the power grid signal is distorted, the method can quickly and accurately extract harmonic components, and it is still robust in the case of white noise, inter-harmonics or high-order harmonics.

Microfluidic technology and its application in the point-of-care testing field

Since the outbreak of the coronavirus disease 2019 (COVID-19), countries around the world have suffered heavy losses of life and property. The global pandemic poses a challenge to the global public health system, and public health organizations around the world are actively looking for ways to quickly and efficiently screen for viruses. Point-of-care testing (POCT), as a fast, portable, and instant detection method, is of great significance in infectious disease detection, disease screening, pre-disease prevention, postoperative treatment, and other fields. Microfluidic technology is a comprehensive technology that involves various interdisciplinary disciplines. It is also known as a lab-on-a-chip (LOC), and can concentrate biological and chemical experiments in traditional laboratories on a chip of several square centimeters with high integration. Therefore, microfluidic devices have become the primary implementation platform of POCT technology. POCT devices based on microfluidic technology combine the advantages of both POCT and microfluids, and are expected to shine in the biomedical field. This review introduces microfluidic technology and its applications in combination with other technologies.

Highly sensitive, selective, and reusable nanofibrous membrane-based carbon polymer dots sensors for detection of Cr(VI) in water

Core-shell structured fluorescent carbon polymer dots (CPDs) were covalently incorporated onto poly(vinyl alcohol-co-ethylene) (PVA-co-PE) nanofibers to fabricate novel nanofibrous membrane sensors to detect trace amount of heavy metal ions in drinking water systems. The membrane sensor was assembled in a syringe filter cell as a sensor unit together with a volumetric syringe, and fluorescent color changes of the sensors responding to concentrations of Cr(VI) can be identified by naked eyes. The sensor system possesses features of instant detection, low cost, environment-friendliness, and high sensitivity to quantitative measure Cr(VI) in a concentration range of 1–50 ppb, which is much lower than federal permissible limit of Cr(VI) in drinking water. Moreover, the used sensor could be refreshed and reused for many times without obvious fluorescence loss and nanofibrous membrane damage by simply exposing to a 20 μM Vitamin C (VC) solution. The fluorescent color changes of the sensors could be read by an App on a smartphone, which is facile, convenient and suitable for more accurate household operation, showing great prospect for instant on-site detection of Cr(VI) in drinking water.

Multi-Frequency Instantaneous Signal Detection Scheme Based on Non-Uniform Optical Frequency Comb

Multi-Frequency Instantaneous Signal Detection Scheme Based on Non-Uniform Optical Frequency Comb

Implementation of rfid in university library: An advanced research at Maharshi Dayanand University, Rohtak

RFID is a cutting-edge integrated library management system that enables the instantaneous detection and recognition of library materials. RFID applications vary from book monitoring and stock management to security in general and completely automated book classifying at the moment. Shortly, RFID systems will play an important role in data collection, recognition, and analysis required for specific library operations. An RFID-enabled automated library might be a “self-service station” that involves reducing the amount of interference from staff members. Efforts are being made to implement self-service “check-in” and “checkout” to prevent long delays in the delivery of library collection and to improve operational efficiency. Considering the significance of library security, this paper focuses on the use of Radio frequency identification (RFID) in libraries, including its elements, advantages, and the role of the librarian. The researcher collected the data of 50 users from Maharshi Dayanand University library, Rohtak, to find out the status of RFID implementation and the satisfaction level of users with the system. Most of the users are satisfied with the component and its benefits of it.

Electromagnetic Reflection Field Detection Based on TEO

Active electromagnetic detection explores targets using electromagnetic reflection field of metal objects. In view of the detection of active electromagnetic by detecting target reflection field, a Teager energy operator(TEO) is adopted to improve the electromagnetic reflection amplitude and instantaneous frequency detection method, the method of theoretical description and simulation verification has also carried out in this paper. Results show that this method is able to track the change of the single frequency signal of electromagnetic reflection field with good effect. It is also able to provide full details of the judgment to confirm the target. This method is simple and convenient for real-time processing, and has good application value.

IoT-edge anomaly detection for covariate shifted and point time series health data

Abrupt distributional covariate shift and abnormal data points can cause anomaly in the time series dataset. Such issue can exaggerate in the internet of things (IoT)-based scenario where continuous data is generated from the low-cost health sensors. Existing anomaly detection techniques mostly rely on the machine learning aware strategies resulting processing and storage overhead in the IoT-based tool chain. In this paper, we perform two experiments on anomaly detection on univarite data such as, (i) anomaly detection of online covariate shift i.e. IoTSAnom method and (ii) seasonal-decomposition by the Loess i.e. STL and piecewise median schemes i.e. IoTSDA method. Firstly, we propose the IoTSAnom architecture to detect online coviarte shift in the univariate time series pulse rate data stream. We implement the ”IRKernel” to investigate the efficiency of exponentially weighted moving average (EWMA)-based light-weight statistical learning by using three variants e.g. probabilistic (P), shift-detect (SD), and two-stage shift-detect (TS-SD) over the EWMA algorithms. We perform evaluation of execution time of the algorithms in classical, incremental, and optimized modes while following the Kolmogorov–Smirnov hypothesis testing at the two-stage anomaly detection. Secondly, the IoTSDA scheme is implemented to support an instantaneous anomaly detection based on the interquartile range and generalized extreme studentized deviate statistics. Both the experimental results show that use of probability, shift-detection scheme, and time-series seasonal-decomposition can detect anomalies. Both the schemes can leverage the IoT-edge analytics for smart health care anomaly detection.

Surface plasmon resonance aptasensor for Brucella detection in milk

A Surface Plasmon Resonance (SPR) aptasensor was developed for the detection of Brucella melitensis (B. melitensis) in milk samples. Brucellosis is a bacterial zoonotic disease with global distribution caused mostly by contaminated milk or their products. Aptamers recognizing B. melitensis were selected following a whole bacteria-SELEX procedure. Two aptamers were chosen for high affinity and high specificity. The high affinity aptamer (B70 aptamer) was immobilized on the surface of magnetic silica core-shell nanoparticles for initial purification of the target bacteria cells from milk matrix. Another aptamer, highly specific for B. melitensis cells (B46 aptamer), was used to prepare SPR sensor chips for sensitive determination of Brucella in eluted samples from magnetic purification since direct injection of milk samples to SPR sensor chips is known for a high background unspecific signal. Thus, we integrated a quick and efficient magnetic isolation step for subsequent instant detection of B. melitensis contamination in one ml of milk sample by SPR with a LOD value as low as 27 ± 11 cells.

Sensitivity analysis of the nonparametric criterion of aircraft flght control system sensors failures detection and isolation

Failures of the aircraft control system sensors can cause both deterioration of stability and controllability characteristics and the inability of safe automatic control. It is necessary to detect and isolate such failures to determine the time and place of their occurrence in order to disable failed sensors or to diagnose them subsequently for reconfiguration during the flight. The direct use of traditional parametric approaches for sensors health monitoring by using their mathematical models is impossible due to the lack of data about the true information input signals received by their sensitive elements. This leads to the necessity of solving the problem of modeling the aircraft flight dynamics with a high level of uncertainties, which makes it difficult to utilize the functional control methods and necessitate the use of excessive sensor hardware redundancy. Well-known nonparametric methods either require a priori knowledge base, preliminary training or long-term tuning on a large volume of real flight data or have low selective sensitivity for reliable detection of failed sensors. In this work, the original nonparametric criterion for detecting and isolating sensors failures is derived. Its sensitivity is analyzed by using a complete nonlinear mathematical model of aircraft flight dynamics with a regular flight control system. The theoretical value and the criterion sensitivity coefficients are determined. The formula for the automatic evaluation of the float criterion threshold value is given. A high convergence of the results with theoretical ones is shown. This makes it possible to use the obtained criterion not only for the instant detection and isolation of sensors failures, but also for preliminary diagnostics of their quantitative characteristics.

Spiropyran and spironaphthoxazine based opto-chemical probes for instant ion detection with high selectivity and sensitivity to trace amounts of cyanide

Colorimetric chemical sensors with instant detection of cyanide ion, as a dangerous effluent from various industries and high toxicity, are nowadays of significant importance. Here, photochromic spironaphthoxazine (SNO) and hydroxyethyl spiropyran (SPOH) were synthesized and employed to detect some cations as well as trace amount of cyanide ion with reasonable selectivity and sensitivity among different anions. Their spiro forms were not triggered by metal cations, while their UV-induced open-ring isomers became susceptible to establish a dye-cation complex. The opto-chemical results demonstrated that neither SPOH nor SNO could make reliable discrimination between different metal cations, even after UV irradiation. However, both SPOH and SNO were selectively attached to CN− with a 1:1 stoichiometric ratio (from Job’s plot), even in the presence of several other anions and in the absence of UV irradiation. This was approved by 1HNMR and UV–vis spectroscopies. Besides, the interaction of CN− with SPOH and SNO was approved by visual color change from colorless (λmax of 345 nm for both) to dark yellow with λmaxs of 416 and 412 nm for those linked to hydroxyethyl merocyanine (MC-CN) and meronaphthoxazine (MNO-CN), respectively. The detection limits of SPOH (0.091 µM) and SNO (0.094 µM) sensors toward CN− were lower than its permitted level in drinking water by WHO (1.9 µM). These were also checked and compared with other spiro-based sensors. The obtained results demonstrate the potentiality of SNO and SPOH as promising colorimetric and opto-chemical sensors with wide linear dynamic detection range for determining trace amount of CN−.

“All-in-One” ultra-portable colorimetric sensor coupled with confinement effect for Hg2+ highly sensitive and instant detection

Due to the low sensitivity of traditional colorimetric sensors, complex and time-consuming signal amplification processes are often required in the actual detection process. In this work, with the aid of the porous nature of the gel matrix, a unique “All-in-One” ultra-portable colorimetric sensor has been constructed for highly sensitive and instant detection of low-abundance Hg2+ without signal amplification. Here, by dropping the prepared “Pill” sensor into the sample solution, the Hg2+ can specifically trigger the colorimetric reaction, so as to realize the on-site instant detection of Hg2+. Under the confinement effect, the porous structure of the gel matrix can serve as a preconcentrator to enrich the analyte, thus enhancing detection sensitivity of the sensor. Additionally, all colorimetric reactants are integrated into a small “Pill”, which greatly improves the portability of the sensor. Such an unusual sensor exhibits an outstanding linear response to the concentration in the range between 0.1 and 100 μM, and the detection limit is as low as 0.04 μM (S/N = 3). The as fabricated assay is sensitive, easy operation, and versatile. It has been successfully used for measuring Hg2+ in four different actual samples, demonstrating great promise for practical applications.

Detecting development scenarios of dynamic events in electric power networks smart-grid Part1 “Method”

A method for constructing a semantic S-detector for the senior hierarchical level of pattern recognition in dynamic objects is proposed. The S-detection method is based on a uniform synthesis of all hierarchically subordinate detectors and information preparation schemes, based on the tasks of the upper hierarchical level of recognition of scenarios for the development of processes in an object. The method can be used to solve problems of relay protection, emergency control automation, to identify rare events in impulse, short-term input signals when implementing the concept of developing of electric power smart-grid. The synthesis of the S-detector of scenarios for the development of transient processes was the result of the development of a structural-information SI-method for processing information components. A mathematical SI-model is presented that allows you to control the mutual correspondence of the structure of the formation tree in the object and the device recognition tree. The task is to improve the stability of the device recognition algorithm. The Part 1 describes the theoretical provisions of the application of the recognition method. It is proposed to unify the trees for generating recognition scenarios in the industry, similar to the IEEE high-voltage network diagrams. The S-detector algorithm based on the formation of a semantic signal and on the selective search for the required amount of information for automatic decision-making is presented. The facts of the appearance of elementary information events in the input signals of the detector are controlled by Ξ,H-selectivity windows, which are sequentially opened depending on the previous development of events. The block diagram of the S-detector of the scenario is presented, which can be synchronous, multichannel, with a high signal processing speed, without losing rapidly changing information when slow and infraslow information is detected. Combines useful properties of instantaneous and inertial detection algorithms. The output of the S-detector is a series of responses with different weights. This allows elementary detectors to be combined into sequential chains at different hierarchical levels of recognition. Part 2 provides an example of the application of the scenario detection method to solve the problem of increasing the stability of the operation of a selective protection relay against single-phase earth faults based on medium-frequency signals in 6-35 kV networks with an arc suppression Petersen’scoil. The results of modellingthe operation of the S-detector algorithm on the signals of real emergency files recorded by high-frequency recorders of transient processes in the network are presented.