Active Detection Method Research Articles

A targeted search for Main Belt Comets

ABSTRACT Main Belt Comets (MBCs) exhibit sublimation-driven activity while occupying asteroid-like orbits in the Main Asteroid Belt. MBCs and candidates show stronger clustering of their longitudes of perihelion around 15° than other objects from the Outer Main Belt (OMB). This potential property of MBCs could facilitate the discovery of new candidates by observing objects in similar orbits. We acquired deep r-band images of 534 targeted asteroids using the Isaac Newton Telescope/Wide Field Camera between 2018 and 2020. Our sample is comprised of OMB objects observed near perihelion, with longitudes of perihelion between 0 and 30° and orbital parameters similar to known MBCs. Our pipeline applied activity detection methods to 319 of these objects to look for tails or comae, and we visually inspected the remaining asteroids. Our activity detection pipeline highlighted a faint antisolar tail-like feature around 2001 NL19 (279870) observed on 2018 November 07, 6 months after perihelion. This is consistent with cometary activity; however, additional observations of this object will be needed during its next perihelion to investigate its potential MBC status. If it is active our survey yields a detection rate of ∼1:300, which is higher than previous similar surveys, supporting the idea of dynamical clustering of MBCs. If not, it is consistent with previously estimated abundance rates of MBCs in the OMB (<1:500).

FarNet-II: An improved solar far-side active region detection method

Context. Activity on the far side of the Sun is routinely studied through the analysis of the seismic oscillations detected on the near side using helioseismic techniques such as phase-shift sensitive holography. Detections made through those methods are limited to strong active regions due to the need for a high signal-to-noise ratio. Recently, the neural network FarNet was developed to improve these detections. This network extracts more information from helioseismic far-side maps, enabling the detection of smaller and weaker active regions. Aims. We aim to create a new machine learning tool, FarNet-II, which further increases FarNet’s scope, and to evaluate its performance in comparison to FarNet and the standard helioseismic method for detecting far-side activity. Methods. We developed FarNet-II, a neural network that retains some of the general characteristics of FarNet but improves the detections in general, as well as the temporal coherence among successive predictions. The main novelties of the new neural network are the implementation of attention and convolutional long short-term memory (ConvLSTM) modules. A cross-validation approach, training the network 37 times with a different validation set for each run, was employed to leverage the limited amount of data available. We evaluate the performance of FarNet-II using three years of extreme ultraviolet observations of the far side of the Sun acquired with the Solar Terrestrial Relations Observatory (STEREO) as a proxy of activity. The results from FarNet-II were compared with those obtained from FarNet and the standard helioseismic method using the Dice coefficient as a metric. Given that the application of the ConvLSTM modules can affect the accuracy as a function of the position on the sequence, we take this potential dependency into account in the evaluation. Results. FarNet-II achieves a Dice coefficient that improves that of FarNet by over 0.2 points for every output position on the sequences from the evaluation dates. Its improvement over FarNet is higher than that of FarNet over the standard method. Conclusions. The new network is a very promising tool for improving the detection of activity on the far side of the Sun given by pure helioseismic techniques. Space weather forecasts can potentially benefit from the higher sensitivity provided by this novel method.

Non-Local Temporal Difference Network for Temporal Action Detection.

As an important part of video understanding, temporal action detection (TAD) has wide application scenarios. It aims to simultaneously predict the boundary position and class label of every action instance in an untrimmed video. Most of the existing temporal action detection methods adopt a stacked convolutional block strategy to model long temporal structures. However, most of the information between adjacent frames is redundant, and distant information is weakened after multiple convolution operations. In addition, the durations of action instances vary widely, making it difficult for single-scale modeling to fit complex video structures. To address this issue, we propose a non-local temporal difference network (NTD), including a chunk convolution (CC) module, a multiple temporal coordination (MTC) module, and a temporal difference (TD) module. The TD module adaptively enhances the motion information and boundary features with temporal attention weights. The CC module evenly divides the input sequence into N chunks, using multiple independent convolution blocks to simultaneously extract features from neighboring chunks. Therefore, it realizes the information delivered from distant frames while avoiding trapping into the local convolution. The MTC module designs a cascade residual architecture, which realizes the multiscale temporal feature aggregation without introducing additional parameters. The NTD achieves a state-of-the-art performance on two large-scale datasets, 36.2% mAP@avg and 71.6% mAP@0.5 on ActivityNet-v1.3 and THUMOS-14, respectively.

An exploratory experiment using temperature drop curve features to identify activity information of duck eggs at mid-incubation

To address the problem that duck egg mortality is not easily detected at mid-incubation, this paper explored a method to detect mid-incubation egg activity information based on temperature drop curve (TDC) features. In this paper, we used a thermal infrared camera to obtain continuous thermal images of death fertilized duck eggs (DFDE) on the 16th day of incubation and alive fertilized duck eggs (AFDE) hatched for 16–19 days in a 20 °C environment. By observing the temperature drop curve of egg surface, we extracted and visualized five features that could reflect the activity information of duck eggs. And we used K-Nearest Neighbor (KNN), Naive Bayesian (NB) and Support Vector Machine (SVM) to establish the activity information detection models for different incubation days. The results showed that KNN could better distinguish the activity of eggs at the 16th and the 17th day of incubation, with F1-score of 85.43% and 85.98%, respectively. The SVM showed better results at the 18th and the 19th day of incubation, with F1-score of 90.57% and 96.3%, respectively. The experimental results demonstrated that the activity detection method based on the temperature drop curve features in this paper could efficiently and nondestructively detect the activity information of mid-incubation duck eggs, which provided a technical foundation for detecting the activity information of duck eggs at mid-incubation.

A control‐switching approach for cyberattack detection in process systems with minimal false alarms

AbstractThe frequency of cyberattacks against process control systems has increased in recent years. This work considers multiplicative false‐data injection attacks involving the multiplication of the data communicated over the sensor‐controller communication link by a factor. An active detection method utilizing switching between two control modes is developed to balance the trade‐off between closed‐loop performance and attack detectability. Under the first mode, the control parameters are selected using traditional control design criteria. Under the second mode, the control parameters are selected to enhance the attack detection capability. A switching condition is imposed to prevent false alarms that could be triggered by the transient response induced by control mode switching. This condition is incorporated into the active detection method to minimize false alarms. The active detection method is applied to illustrative process examples to demonstrate its ability to detect attacks and minimize false alarms.

Applying Deep Learning and Computer Vision Techniques for an e-Sport and Smart Coaching System Using a Multiview Dataset: Case of Shotokan Karate

Smart coaching and e-sport platforms have shown a great interest in the recent research studies. Through this study, we aim to globalize the practice of sport, especially Shotokan Karate, by connecting participants and coaches on an international scale through the integration of Artificial Intelligence techniques such as computer vision and deep learning, to give the possibility of carrying out national and international virtual training courses without logistical constraints. The proposed work aims to apply the latest action detection, action recognition, and action classification methods for different Karate movements using the LSTM and the ST-GCN algorithms and proposes these movements in 3D using Video Inference for Human Body Pose and Shape Estimation (VIBE). Our proper Multiview Dataset contains pose estimations of a set of basic movements captured by a karate Shotokan expert (6th DAN Black Belt) from three views (Front view, Left view, and Right view) using OpenPose and FastPose for human body keypoint detection. The current study sets out to detect, recognize, classify and score different participants' movements. We achieved greater than 96% recognition accuracy of this dataset using the LSTM algorithm, and 91.01% using the ST-GCN algorithm.

Multidimensional Analysis of Physical Education Teaching Based on Big Data Action Scenarios

Combined with the development of the current educational environment, physical education will replace English as the third subject of education in the future. The breakthrough in physical education teaching is also gradually changing from the general education form to today’s smart education. In the era of big data, physical education can also apply this technology. In action scenarios based on big data, operations such as correction of detailed actions or monitoring and identification of key actions are common. Through the computer vision system, the rational judgment of the computer can be used to give follow-up training points. Also able to store personal data during training. The presentation of algorithms cannot be avoided through computer vision. Based on Action Bank as the basic algorithm, this paper proposes a template research method based on multispectral clustering and has been applied in Action Bank. The tedious manual template selection is eliminated. This method replaces it to facilitate its dissemination in different databases. In this method, due to the slow speed of extracting features, a fast algorithm of quantitative Action Bank is extracted. The experimental part of the article compares whether the algorithm has been optimized in terms of performance before and after optimization. The resolution, time consumption, and detection errors of the Action Bank model are carried out. The experimental exploration and data collection and comparison are carried out. After the experimental optimization, the performance has been improved. By comparing the meanshift detection method and spatiotemporal action detection method with the Action Bank model mentioned in this article, the experimental data of resolution, time consumption, and detection error are compared, and the Action Bank model is obtained in terms of resolution, time consumption, and error detection. The time consumption is better than the other two algorithm models, but there is room for improvement in detecting errors, but the experimental results also meet the current detection requirements, and in the current physical education teaching, it also occupies the forefront of this field, at the forefront. Ministry of Education. Instead of English, physical education has become the third subject! On April 21, the Ministry of Education released the “Compulsory Education Physical Education and Health Curriculum Standard (2022 Edition),” and the new curriculum standard will be officially implemented in the fall semester of 2022. Among them, the proportion of the total class hours of “Sports and Health” is 10-11%, surpassing foreign language to become the third major subject in the primary and early stages. Physical education has already started its journey. Traditional physical education can no longer meet the development of the current environment. The arrival of the information society has also brought about the development and progress of physical education. Only by better combining the current information technology can it be satisfied. The needs of physical education is in the future.

Quality Evaluation and Informatization Analysis of Physical Education Teaching Reform Based on Artificial Intelligence

With the continuous reform of China’s education and the development of the educational environment, English will no longer be the third subject for Chinese students and physical education will replace it as the third subject. The teaching mode has also gradually changed from the traditional artificial mode to the current smart education. In the context of artificial intelligence, physical education can also apply this technology to daily teaching. After physical education becomes the third subject, it is necessary to reform the existing teaching mode and conduct quality evaluation and informatization analysis. In order to achieve this effect, we use artificial intelligence action scenes to detect students' detailed actions and identify key actions and then use computer vision system to create regression models and Bayesian formulas to give the criteria for judging the subsequent training points of the computer. Then, according to the training data of each student in the training process, the quality analysis and informatization evaluation of the teaching reform of physical education are carried out. Using the action bank algorithm as the basic algorithm of feature extraction, a template research method based on multispectral clustering is proposed to facilitate its dissemination in the computer background database. Then, through experiments to compare the before and after optimization of the algorithm, the data analysis of the resolution, time consumption, and detection error of the action bank model were carried out, and it was found that the performance was improved. Then, by means of mean shift detection method and spatiotemporal action detection method, the resolution, time consumption, and detection error of the action bank model are optimized to achieve the quality evaluation and informatization analysis of physical education teaching reform.

The study of early screening technique for fruiting ability of Lentinula edodes hybrid progenies.

Crossbreeding is the most commonly used method in breeding of Lentinula edodes, however low fruiting rate of the hybrids has always caused troubles and barriers for breeders. An early screening method of the fruiting ability could make the breeding work more efficient. In this paper, a rapid and high-throughput laccase activity detection method based on agar diffusion principle was developed. In this way, we investigated the constitutive and inducible extracellular laccase activity of 36 strains in a breeding population of L. edodes on different media and performed a correlation analysis with fruiting ability of these strains. The results showed the laccase activity of mycelium cultured in non-induced medium for 8 d could be used as an early screening index to judge whether it had fruiting ability at the later stage. Early rapid and simple screening method for hybrid populations was established based on laccase activity characteristics of mycelia. 127 strains from another 5 different hybrid populations were used to verify the early screening method. From the validation results, the early screening method was effective, but the appropriate screening threshold was needed to select according to the cross population, which would greatly to improve the breeding efficiency of L. edodes.

Multi action recognition using machine learning

One of the most exciting and useful computer vision research topics is automated human activity identification. The majority of existing research, as well as most traditional approaches and classic neural networks, disregard the In video sequences, the appearance and patterns of motion are important. Individuals are unableIn video sequences, the appearance and patterns of motion are important.. This paper outlines a system for detecting, recognising, and summarising diverse human actions.The multiple action detection method takes the silhouettes of human bodies and then uses motion detection and tracking to create a unique sequence for each one.Based on the Each of the recovered sequences is then separated into shots based on the similarity between each pair of frames.show the sequence's homogenous activity. The activity is identified by looking at thedata oriented gradient (HOG) histogram of the frames in each shot's Temporal Difference Map (TDMap). comparing generated HOG to previous HOGs in the early stages of training. Making use of the TDMap image and a proposed CNN model, we can additionally recognise the action. Action summarising is carried out for each person found.

Development of an Interactive Total Body Robot Enhanced Imitation Therapy for ASD children.

Autism is a neurodevelopmental disorder in which the available therapies target the improvement of social skills, in order to ensure a high quality of life for the child. The use of Social Assistive Robots offers new therapeutic possibilities in which robots can act as therapy enhancers. IOGIOCO project emerges in this framework: it aims at the development of a Robot- Assisted Therapy protocol for the treatment of Autism Spectrum Disorder, through gesture training. The definition of these gestures and their recognition by the robot are parameters that directly affect the engagement of the children. However, the design of a protocol becomes harder in a highly unconstrained environment. Therefore, the current work aims at expanding the gesture set and improving the gesture recognition algorithm available in the IOGIOCO platform. More specifically, total body gestures have been added to the available upper limbs movements, and a custom Activity Detection method has been developed, which allows the identification of the time window in which a gesture is performed. The insertion of this method on a recognition algorithm based on a ResNet, a particular kind of Convolutional Neural Network, improved its F1-score from 57% obtained with the previously-available version, in a dataset of ASD children, to 76%, demonstrating the effectiveness of the Activity Detection method. Furthermore, the expansion of the interaction possibilities to total body movements was positively evaluated by the clinical staff, increasing the engagement of patients and the set of possible trained skills. Therefore, the results of the current work are encouraging. To reinforce the conclusions drawn, the proposed algorithm should be tested in real time on several autistic children within a complete Randomized Clinical Trial, also to study the effectiveness of this type of treatment. From the technical point of view, further improvements of the developed methodology should tackle the remained issues, such as further increasing the recognition capability, especially in the transitions from sitting to standing, that proved to be a hard task for the developed method.

Passive detection in water pipelines using ambient noise II: Field experiments

A passive detection method has been proposed in a prior paper to extract key parameters and detect faults using the ambient noise present in water pipeline networks. This paper presents field experiments and data processing results to provide systematic experimental validation of this method. Field experiments were carried out in operational water pipeline networks at the University of Canterbury campus and the Waimakariri District, New Zealand, during which ambient noise was measured by pairs of pressure sensors installed at selected hydrants on pipelines of different materials, network topologies and simulated faults. Auto-correlation and cross-correlation analysis of noise at a single sensor and sensor pairs were carried out to estimate the wave speed and to locate faults in the networks. Data processing results indicate that water usage generating pressure transients are the dominant sources of ambient noise in operational water pipeline networks. This type of ambient noise can also be utilized by the passive detection method to achieve similar wave speed estimation accuracy and fault detection performance as the conventional active pressure wave detection methods.

Weakly Supervised Temporal Action Detection With Temporal Dependency Learning

Weakly supervised temporal action detection aims at localizing temporal positions of action instances in untrimmed videos with only action class labels. In general, previous methods individually classify each frame based on the appearance information and the short-term motion information, and then integrate consecutive high-response action frames into entities which serve as detected action instances. However, the long-range temporal dependencies between action frames are not fully utilized, and the detection results are more likely to be trapped in the most discriminative action segments. To alleviate this issue, we propose a novel two-branch (i.e., the coarse detection branch and the refining detection branch) detection framework with learning the long-range temporal dependencies for obtaining more accurate detection results, where only action class labels are required. The coarse detection branch is used to localize the most discriminative segments of action instances based on a typical multi-instance learning paradigm under the supervision of action class labels, whereas the refining detection branch is expected to localize the less discriminative segments of action instances via learning the long-range temporal dependencies between frames based on the proposed Transformer-style architecture and learning strategies. This collaboration mechanism takes full advantage of complementary information from the provided action class labels and the natural temporal dependencies between action frames, forming a more comprehensive solution. Consequently, our method obtains more precise detection results. Expectedly, the proposed method outperforms recent weakly supervised temporal action detection methods on dataset THUMOS14 and ActivityNet measured by mAP@tIoU and AR@AN.

The Effect of Calycosin-7-O-β-D-Glucoside and its Synergistic Augmentation of Cisplatin-induced Apoptosis in SK-OV-3 Cells.

This study aims to examine the synergetic augmentation of calycosin-7-O-β-D-glucoside (CG) on cisplatin (CDDP) to induce apoptosis of human epithelial ovarian SK-OV-3 cancer cells. The SK-OV-3 cells were divided into four groups: control, CDDP monotherapy, CG monotherapy, and combined CDDP and CG treatment. The cell counting kit-8 method detected cell proliferation at different times and under different treatments. Hoechst 33258 staining and annexin V-FITC/propidium iodide double staining methods were used to observe the apoptosis of the SK-OV-3 cells. The caspase-3 enzyme activity detection method, quantitative reverse transcription-polymerase chain reaction, and western blot were used to detect the apoptosis-related factors and the activities of the enzyme in SK-OV-3 cells. The inhibition rates of SK-OV-3 cell proliferation when exposed to 10 μM of CDDP, 50 μM of CG, and a combination of 10 μM of CDDP and 50 μM of CG were 23.2% ± 1.1%, 26.7% ± 2.0%, and 46.7% ± 1.3% after 48 h, respectively. Following the use of the drug combination, the apoptosis rate and caspase-3 enzyme activity were significantly higher than in the single-drug treatment group; the data differences were also significant (p < 0.05). At the protein and ribonucleic acid levels, CG significantly enhanced the effect of CDDP on p53, caspase-3, caspase-9, Bax, and Bcl-2. In vitro, CG significantly increases the CDDP-induced apoptosis of the SK-OV-3 cells through the p53 pathway at the cellular level. In addition, using the drugs in combination reduces the toxicity and side effects caused by using CDDP alone.

SEEG-Net: An explainable and deep learning-based cross-subject pathological activity detection method for drug-resistant epilepsy

ObjectivePrecise preoperative evaluation of drug-resistant epilepsy (DRE) requires accurate analysis of invasive stereoelectroencephalography (SEEG). With the tremendous breakthrough of Artificial intelligence (AI), previous studies can help clinical experts to identify pathological activities automatically. However, they still face limitations when applied in real-world clinical DRE scenarios, such as sample imbalance, cross-subject domain shift, and poor interpretability. Our objective is to propose a model that can address the above problems and realizes high-sensitivity SEEG pathological activity detection based on two real clinical datasets. Methods. Our proposed innovative and effective SEEG-Net introduces a multiscale convolutional neural network (MSCNN) to increase the receptive field of the model, and to learn SEEG multiple frequency domain features, local and global features. Moreover, we designed a novel focal domain generalization loss (FDG-loss) function to enhance the target sample weight and to learn domain consistency features. Furthermore, to enhance the interpretability and flexibility of SEEG-Net, we explain SEEG-Net from multiple perspectives, such as significantly different features, interpretable models, and model learning process interpretation by Grad-CAM++. Results. The performance of our proposed method is verified on a public benchmark multicenter SEEG dataset and a private clinical SEEG dataset for a robust comparison. The experimental results demonstrate that the SEEG-Net model achieves the highest sensitivity and is state-of-the-art on cross-subject (for different patients) evaluation, and well deal with the known problems. Besides, we provide an SEEG processing and database construction flow, by maintaining consistency with the real-world clinical scenarios. Significance. According to the results, SEEG-Net is constructed to increase the sensitivity of SEEG pathological activity detection. Simultaneously, we settled certain problems about AI assistance in clinical DRE, built a bridge between AI algorithm application and clinical practice.

Detection of Water Leaks in Suburban Distribution Mains with Lift and Shift Vibro-Acoustic Sensors

Leaks in Water Distribution Networks (WDNs) account for a large proportion of Non-Revenue Water (NRW) for utilities worldwide. Typically, a leak is only confirmed once water surfaces, allowing the leak to be traced; however, a high percentage of leaks may never surface, incurring large water losses and costs for utilities. Active Leak Detection (ALD) methods can be used to detect hidden leaks; however, the success of such methods is highly dependent on the available detection instrumentation and the experience of the operator. To aid in the detection of both hidden and surfacing leaks, deployment of vibro-acoustic sensors is being increasingly explored by water utilities for temporary structural health monitoring. In this paper, data were collected and curated from a range of temporary Lift and Shift (L&amp;S) vibro-acoustic sensor deployments across suburban Sydney. Time-frequency and frequency-domain features were generated to assess the performance and suitability of two state-of-the-art binary classification models for water leak detection. The results drawn from the extensive field data sets are shown to provide reliable leak detection outcomes, with accuracies of at least 97% and low false positive rates. Through the use of such a reliable leak detection system, utilities can streamline their leak detection and repair processes, effectively mitigating NRW and reducing customer disruptions.

A coarse-to-fine temporal action detection method combining light and heavy networks

Temporal action detection aims to judge whether there existing a certain number of action instances in a long untrimmed videos and to locate the start and end time of each action. Even though the existing action detection methods have shown promising results in recent years with the widespread application of Convolutional Neural Network (CNN), it is still a challenging problem to accurately locate each action segment while ensuring real-time performance. In order to achieve a good tradeoff between detection efficiency and accuracy, we present a coarse-to-fine hierarchical temporal action detection method by using multi-scale sliding window mechanism. Since the complexity of the convolution operator is proportional to the number and the size of the input video clips, the idea of our proposed method is to first determine candidate action proposals and then perform the detection task on these candidate action proposals only with a view to reducing the overall complexity of the detection method. By making full use of the spatio-temporal information of video clips, a lightweight 3D-CNN classifier is first used to quickly determine whether the video clip is a candidate action proposal, avoiding the re-detection of a large number of non-action video clips by the heavyweight deep network. A heavyweight detector is designed to further improve the accuracy of action positioning by considering both boundary regression loss and category loss in the target loss function. In addition, the Non-Maximum Suppression (NMS) is performed to eliminate redundant detection results among the overlapping proposals. The mean Average Precision (mAP) is 40.6%, 51.7% and 20.4% on THUMOS14, ActivityNet and MPII Cooking dataset when the Intersection-over-Union (tIoU) threshold is set to 0.5, respectively. Experimental results show the superior performance of the proposed method on three challenging temporal activity detection datasets while achieving real-time speed. At the same time, our method can generate proposals for unseen action classes with high recalls.

The Classification of Abnormal Hand Movement to Aid in Autism Detection: Machine Learning Study

Background A formal autism diagnosis can be an inefficient and lengthy process. Families may wait several months or longer before receiving a diagnosis for their child despite evidence that earlier intervention leads to better treatment outcomes. Digital technologies that detect the presence of behaviors related to autism can scale access to pediatric diagnoses. A strong indicator of the presence of autism is self-stimulatory behaviors such as hand flapping. Objective This study aims to demonstrate the feasibility of deep learning technologies for the detection of hand flapping from unstructured home videos as a first step toward validation of whether statistical models coupled with digital technologies can be leveraged to aid in the automatic behavioral analysis of autism. To support the widespread sharing of such home videos, we explored privacy-preserving modifications to the input space via conversion of each video to hand landmark coordinates and measured the performance of corresponding time series classifiers. Methods We used the Self-Stimulatory Behavior Dataset (SSBD) that contains 75 videos of hand flapping, head banging, and spinning exhibited by children. From this data set, we extracted 100 hand flapping videos and 100 control videos, each between 2 to 5 seconds in duration. We evaluated five separate feature representations: four privacy-preserved subsets of hand landmarks detected by MediaPipe and one feature representation obtained from the output of the penultimate layer of a MobileNetV2 model fine-tuned on the SSBD. We fed these feature vectors into a long short-term memory network that predicted the presence of hand flapping in each video clip. Results The highest-performing model used MobileNetV2 to extract features and achieved a test F1 score of 84 (SD 3.7; precision 89.6, SD 4.3 and recall 80.4, SD 6) using 5-fold cross-validation for 100 random seeds on the SSBD data (500 total distinct folds). Of the models we trained on privacy-preserved data, the model trained with all hand landmarks reached an F1 score of 66.6 (SD 3.35). Another such model trained with a select 6 landmarks reached an F1 score of 68.3 (SD 3.6). A privacy-preserved model trained using a single landmark at the base of the hands and a model trained with the average of the locations of all the hand landmarks reached an F1 score of 64.9 (SD 6.5) and 64.2 (SD 6.8), respectively. Conclusions We created five lightweight neural networks that can detect hand flapping from unstructured videos. Training a long short-term memory network with convolutional feature vectors outperformed training with feature vectors of hand coordinates and used almost 900,000 fewer model parameters. This study provides the first step toward developing precise deep learning methods for activity detection of autism-related behaviors.

An innovative hybrid method for islanding detection using fuzzy classifier for different circumstances including NDZ

Distributed energy resources (DERs) are an alternative to conventional fossil fuel-based energy resources for a pollution-free operation. But the integration of these DERs to the main grid comes with certain challenges, out of which unintentional islanding detection is of prior importance. A lot of techniques have been used for detecting islanding, among these techniques, passive and active. Passive islanding detection methods have drawbacks in the form of the existence of large non-detection zones whereas active detection methods have devastating effects on power quality. To overcome these drawbacks, a proposed hybrid passive-active systematic methodology based on a smart classifier that decides to use an active method instead of a passive one; is presented. The simulation results confirm that the proposed scheme is accurate, robust, fast, simple to implement for inverter-based DGs, and is able enough to perform effective and rapid detection of islanding in all the circumstances for the chosen system model without degrading power quality. The proposed scheme was simulated using the MATLAB/Simulink platform, and the results reflect its potential with a comparative study.

Validation of the peak frequency of bipolar electrograms for detection of residual conduction in atrial scar tissue

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Abbott Background Activation and voltage (Bi-V) maps (Panel A) based on conventional bipolar electrograms (EGMs) are influenced by both near-field (NF) and far-field (FF) EGM components. This represents a limitation in the accurate detection of residual conduction within regions of scar. Peak frequency (PF) EGM analysis may better distinguish NF from FF activation vs conventional detection methods. (Panels B &amp; C) Purpose 1. To validate the use of PF to detect residual conduction in a well-defined model of narrow isthmus of conduction such as subacute pulmonary vein (PV) reconnection following atrial fibrillation ablation. 2. To compare discrimination value of PF and conventional Bi-V to detect such isthmuses. Methods Bi-V and activation maps were acquired during redo PV isolation (PVI) procedures using a rectangular 16-pole catheter (HD-Grid). LA-PV conduction was assessed during coronary sinus (CS) pacing (500ms cycle length). Any conduction gap (GAP) site at which PVI was established using ≤3 focal radiofrequency applications was analyzed. (Panel A) EGMʼs ≤1cm from the site of PVI were classified as GAP (vs EGMʼs ≥1cm - No-Gap). (Panel B) Results 28 GAPs were found in 12 of 14 consecutive patients in the study. 3,976 EGMʼs (1,547 GAP vs 2,429 No-GAP) were analyzed. GAP regions showed significantly higher PF than No-GAP regions (345 ±135 vs 181±116 Hz, P&amp;lt;0.0001) (Panel E). GAP regions also showed significantly higher BiV than No-GAP regions (1.86±2.11 mV vs 0.62±1.22 mV, P&amp;lt;0.0001) (Panel D). ROC curves for GAP vs No-GAP discrimination were better for PF (AUC 0.84) than for Bi-V (AUC 0.74) with optimal cutoffs of 240 Hz and 0.2 mV, respectively. (Panel F) Conclusion PF better detects residual conduction within an atrial scar region than conventional Bi-V. The PF cutoff value for gap discrimination in the PV antra is 240 Hz.