Neural Network-based Algorithm Research Articles

A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy.

Cleanliness scores in small-bowel capsule endoscopy (SBCE) have poor reproducibility. The aim of this study was to evaluate a neural network-based algorithm for automated assessment of small-bowel cleanliness during capsule endoscopy. 600 normal third-generation SBCE still frames were categorized as "adequate" or "inadequate" in terms of cleanliness by three expert readers, according to a 10-point scale, and served as a training database. Then, 156 third-generation SBCE recordings were categorized in a consensual manner as "adequate" or "inadequate" in terms of cleanliness; this testing database was split into two independent 78-video subsets for the tuning and evaluation of the algorithm, respectively. Using a threshold of 79 % "adequate" still frames per video to achieve the best performance, the algorithm yielded a sensitivity of 90.3 %, specificity of 83.3 %, and accuracy of 89.7 %. The reproducibility was perfect. The mean calculation time per video was 3 (standard deviation 1) minutes. This neural network-based algorithm allowing automatic assessment of small-bowel cleanliness during capsule endoscopy was highly sensitive and paves the way for automated, standardized SBCE reports.

Efficient Real-Time Human Detection Using Unmanned Aerial Vehicles Optical Imagery

ABSTRACT Unmanned Aerial Vehicles (UAVs) are promising technologies within many different application scenarios including human detection in search and rescue and surveillance use cases, which have received considerable attention worldwide. However, adverse conditions, such as varying altitude, overhead camera placement, changing illumination and moving platform, impose challenges for high-performance yet cost-efficient human detection. To overcome these challenges, we propose a novel combination of dilated convolutions with Path Aggregation Network (PAN) as a new deep neural network-based human detection algorithm in real time. Furthermore, we establish a comprehensive human detection dataset with varying backgrounds, illuminations, and contrast and train the proposed machine-learning model on the collected dataset. Our approach achieves both high precision (88.0% mean Average Precision (mAP)) and real time (67.0 Frames Per Second (FPS)) on a commercial off-the-shelf PC platform. In terms of accuracy, the result is comparable to the standard You Only Look Once v3 (YOLOv3). However, the speed is twice as that of the standard YOLOv3. YOLOv4 is slightly more accurate (89.8%) than our approach. However, it is slower (38.0 versus 67.0 FPS) and has more Billion Floating-Point Operations (BFLOPS). The proposed algorithm has also trained with the VisDrone2019 dataset and compared with seven studies using this dataset. The results have further validated the effectiveness of the proposed approach. Moreover, the algorithm has been evaluated on an embedded system (Jetson AGX Xavier), which demonstrates the usefulness of this method on power-constrained devices. The proposed algorithm is fast, memory efficient, and computationally less expensive to achieve high detection performance. It is expected to contribute significantly to the wider use of UAV applications including search and rescue missions to locate missing people, and surveillance particularly for applications running on resource-constrained platforms, like smartphones or tablets. This proposed system is now being used in aerial drone system of Police of Scotland to help them locate and find missing and vulnerable people. The results of the project were broadcasted by BBC Scotland.

Оптимизация тихоходного синхронного генератора модульного типа и принцип реализации системы управления напряжением генератора на основе нейронной сети

A method for optimizing the parameters of a modular half-speed synchronous generator with permanent magnets (PMSG) and the generator voltage control system with a neural network-based algorithm are proposed. The basic design scheme of the modular half-speed PMSG is considered, which features a compact layout of the generator main parts, thereby ensuring the optimal use of the working volume, smaller sizes of the magnetic system, and smaller mass of the active materials used in manufacturing the machine. Owing to the simple and reliable design of the generator, its output parameters can be varied in a wide range with using standard electrical circuits for voltage stabilization and current rectification along with an additional voltage regulation unit. Owing to this feature, the design scheme of the considered generator has essential advantages over the existing analogs with a common cylindrical magnetic core. In view of these circumstances, the development of a high-efficient modular half-speed PMSG as an autonomous DC power source is of both scientific and practical interest; this generator can be used to supply power to a large range of electricity consumers located in rural areas, low-rise residential areas, military communities, allotments etc. In solving the problem of optimizing the generator’s magnetic system, the main electrical machine analysis equation is obtained. The optimal ratios of the winding wire mass to the mass of permanent magnets and of the PM height to the air gap value for achieving the maximum specific useful power output have been determined. An analytical correlation between the optimal design parameters of a half-speed modular PMSG and its power performance parameters has been established. The expediency to develop a neural network-based control system is shown. The number of load-bearing modules of the half-speed PMSG is determined depending on the wind velocity, load factor and the required output voltage. The neural network was trained on the examples of a training sample using a laboratory test bench. The neural network was implemented in the MatLab 2019b environment by constructing a synchronous generator simulation model in the Simulink software extension. The possibility of using the voltage control system of a half-speed modular PMSG with a microcontroller for operation of the neural network platform of the Arduino family (ArduinoDue) independently of the PC is shown.

Classifying and tracking rehabilitation interventions through machine-learning algorithms in individuals with stroke.

IntroductionStroke is the leading cause of disability worldwide. It has been well-documented that rehabilitation (rehab) therapy can aid in regaining health and function for individuals with stroke. Yet, tracking in-home rehab continues to be a challenge because of a lack of resources and population-scale demands. In order to address this gap, we implemented a methodology to classify and track rehab interventions in individuals with stroke.MethodsWe developed personalized classification algorithms, including neural network-based algorithms, to classify four rehab exercises performed by two individuals with stroke who were part of a week-long therapy camp in Jamaica, a low- and middle-income country. Accelerometry-based wearable sensors were placed on each upper and lower limb to collect movement data during therapy.ResultsThe classification accuracy for traditional and neural network-based algorithms utilizing feature data (e.g., number of peaks) from the sensors ranged from 64 to 94%, respectively. In addition, the study proposes a new method to assess change in bilateral mobility over the camp duration.ConclusionThe results of this pilot study indicate that personalized supervised learning algorithms can be used to classify and track rehab activities and functional outcomes in resource limited settings such as LMICs.

Intelligent Learning Algorithm for English Flipped Classroom Based on Recurrent Neural Network

Reading and writing are the foundations of English learning as well as an important method of instruction. With the advancement of network technology and the onset of the information age, an increasing number of students have lost interest in traditional English reading and writing instruction in the classroom. Flipped classrooms have emerged as a result of this situation and have become the focus of research in one fell swoop. As a result, flipped classroom research at home and abroad has primarily focused on the theory and practical application of flipped classrooms, and flipped classroom application practice is primarily based on the overall classroom, with few separate discussions on the effects of flipped classroom students’ self‐learning. As a result, we developed a recurrent neural network‐based intelligent assisted learning algorithm for English flipped classrooms. There are two main characteristics of the model. First, it is a gated recurrent unit based on a variant structure of the recurrent neural network. The double‐gating mechanism fully considers the context and selects memory through weight assignment, and on this basis, it integrates the novel LeakyReLU function to improve the model’s training convergence efficiency. Second, by overcoming time‐consuming problems in the medium, the adoption of the connection sequence classification algorithm eliminates the need for prior alignment of speech and text data, resulting in a direct boost in model training speed. The experimental results show that in the English flipped classroom’s intelligent learning mode, students explore and discover knowledge independently, their enthusiasm and interest in learning are greatly increased, and the flipped classroom’s teaching effect is greatly improved.

Optimisation algorithm-based recurrent neural network for big data classification

Optimisation algorithm-based recurrent neural network for big data classification

Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks

Due to the unique feature of the three-dimensional convolution neural network, it is used in image classification. There are some problems such as noise, lack of labeled samples, the tendency to overfitting, a lack of extraction of spectral and spatial features, which has challenged the classification. Among the mentioned problems, the lack of experimental samples is the main problem that has been used to solve the methods in recent years. Among them, convolutional neural network-based algorithms have been proposed as a popular option for hyperspectral image analysis due to their ability to extract useful features and high performance. The traditional convolutional neural network (CNN) based methods mainly use the two-dimensional CNN for feature extraction, which makes the interband correlations of HSIs underutilized. The 3-D-CNN extracts the joint spectral-spatial information representation, but it depends on a more complex model. To address these issues, the report uses a 3-D fast learning block (depthwise separable convolution block and a fast convolution block) followed by a 2-D convolutional neural network was introduced to extract spectral-spatial features. Using a hybrid CNN reduces the complexity of the model compared to using 3-D-CNN alone and can also perform well against noise and a limited number of training samples. In addition, a series of optimization methods including batch normalization, dropout, exponential decay learning rate, and L2 regularization are adopted to alleviate the problem of overfitting and improve the classification results. To test the performance of this hybrid method, it is performed on the Salinas, University Pavia and Indian Pines datasets, and the results are compared with 2-D-CNN and 3-D-CNN deep learning models with the same number of layers.

Synthetic computed tomography data allows for accurate absorbed dose calculations in a magnetic resonance imaging only workflow for head and neck radiotherapy.

Few studies on magnetic resonance imaging (MRI) only head and neck radiation treatment planning exist, and none using a generally available software. The aim of this study was to evaluate the accuracy of absorbed dose for head and neck synthetic computed tomography data (sCT) generated by a commercial convolutional neural network-based algorithm. For 44 head and neck cancer patients, sCT were generated and the geometry was validated against computed tomography data (CT). The clinical CT based treatment plan was transferred to the sCT and recalculated without re-optimization, and differences in relative absorbed dose were determined for dose-volume-histogram (DVH) parameters and the 3D volume. For overall body, the results of the geometric validation were (Mean±1sd): Mean error -5±10HU, mean absolute error 67±14HU, Dice similarity coefficient 0.98±0.05, and Hausdorff distance difference 4.2±1.7mm. Water equivalent depth difference for region Th1-C7, mid mandible and mid nose were -0.3±3.4, 1.1±2.0 and 0.7±3.8mm respectively. The maximum mean deviation in absorbed dose for all DVH parameters was 0.30% (0.12Gy). The absorbed doses were considered equivalent (p-value<0.001) and the mean 3D gamma passing rate was 99.4 (range: 95.7-99.9%). The convolutional neural network-based algorithm generates sCT which allows for accurate absorbed dose calculations for MRI-only head and neck radiation treatment planning. The sCT allows for statistically equivalent absorbed dose calculations compared to CT based radiotherapy.

A neural network-based algorithm for high-throughput characterisation of viscoelastic properties of flowing microcapsules.

Microcapsules, consisting of a liquid droplet enclosed by a viscoelastic membrane, have a wide range of biomedical and pharmaceutical applications and also serve as a popular mechanical model for biological cells. In this study, we develop a novel high throughput approach, by combining a machine learning method with a high-fidelity mechanistic capsule model, to accurately predict the membrane elasticity and viscosity of microcapsules from their dynamic deformation when flowing in a branched microchannel. The machine learning method consists of a deep convolutional neural network (DCNN) connected by a long short-term memory (LSTM) network. We demonstrate that with a superior prediction accuracy the present hybrid DCNN-LSTM network can still be faster than a conventional inverse method by five orders of magnitude, and can process thousands of capsules per second. We also show that the hybrid network has fewer restrictions compared with a simple DCNN.

Image Analysis Using Machine Learning for Automated Detection of Hemoglobin H Inclusions in Blood Smears - A Method for Morphologic Detection of Rare Cells

Background: Morphologic rare cell detection is a laborious, operator-dependent process which has the potential to be improved by the use of image analysis using artificial intelligence. Detection of rare hemoglobin H (HbH) inclusions in red cells in the peripheral blood is a common screening method for alpha-thalassemia. This study aims to develop a convolutional neural network-based algorithm for the detection of HbH inclusions. Methods: Digital images of HbH-positive and HbH-negative blood smears were used to train and test the software. The software performance was tested on images obtained at various magnifications and on different scanning platforms. Another model was developed for total red cell counting and was used to confirm HbH cell frequency in alpha-thalassemia trait. The threshold minimum red cells to image for analysis was determined by Poisson modeling and validated on image sets. Results: The sensitivity and specificity of the software for HbH+ cells on images obtained at ×100, ×60, and ×40 objectives were close to 91% and 99%, respectively. When an AI-aided diagnostic model was tested on a pilot of 40 whole slide images (WSIs), good inter-rater reliability and high sensitivity and specificity of slide-level classification were obtained. Using the lowest frequency of HbH+ cells (1 in 100,000) observed in our study, we estimated that a minimum of 2.4 × 106 red cells would need to be analyzed to reduce misclassification at the slide level. The minimum required smear size was validated on 78 image sets which confirmed its validity. Conclusions: WSI image analysis can be utilized effectively for morphologic rare cell detection. The software can be further developed on WISs and evaluated in future clinical validation studies comparing AI-aided diagnosis with the routine diagnostic method.

Application of Flipped Classroom Model Driven by Big Data and Neural Network in Oral English Teaching

With the advancement of big data and neural network technology, flipped classroom informatization has shifted the traditional order of knowledge transfer and internalization, emphasizing students’ autonomous learning before class, knowledge absorption, and knowledge completion in class with the assistance of teachers. Students’ internalization and consolidation create the conditions for individualized learning. In foreign teaching, the benefits and feasibility of the flipped classroom have been demonstrated, and it is a promising new teaching model. Although recent research on oral English teaching in Chinese universities has yielded promising results, students’ classroom activity and participation remain low, learning initiative is lacking, and opportunities and time for oral training are insufficient. This article uses flipped classroom, big data, and neural network technology to teach college oral English classes, with the goal of determining whether the flipped classroom model can help students improve their oral English proficiency and self‐learning ability, as well as exploring students’ attitudes toward the flipped classroom model. This paper first proposes a big data and deep neural network‐based algorithm for detecting oral English pronunciation errors, which can be used for self‐correction of students in the flipped classroom mode to improve the quality of oral English teaching. Finally, we also conducted simulation experiments, and the experimental results show that our algorithm is 4.12% better than SVM.

A Bibliometric of Publication Trends in Medical Image Segmentation: Quantitative and Qualitative Analysis

Purpose: Medical images are important in diagnosing disease and treatment planning. Computer algorithms that describe anatomical structures that highlight regions of interest and remove unnecessary information are collectively known as medical image segmentation algorithms. The quality of these algorithms will directly affect the performance of the following processing steps. There are many studies about the algorithms of medical image segmentation and their applications, but none involved a bibliometric of medical image segmentation. Methods: This bibliometric work investigated the academic publication trends in medical image segmentation technology. These data were collected from the Web of Science (WoS) Core Collection and the Scopus. In the quantitative analysis stage, important visual maps were produced to show publication trends from five different perspectives including annual publications, countries, top authors, publication sources, and keywords. In the qualitative analysis stage, the frequently used methods and research trends in the medical image segmentation field were analyzed from 49 publications with the top annual citation rates. Results: The analysis results showed that the number of publications had increased rapidly by year. The top related countries include the Chinese mainland, the United States, and India. Most of these publications were conference papers, besides there are also some top journals. The research hotspot in this field was deep learning-based medical image segmentation algorithms based on keyword analysis. These publications were divided into three categories: reviews, segmentation algorithm publications, and other relevant publications. Among these three categories, segmentation algorithm publications occupied the vast majority, and deep learning neural network-based algorithm was the research hotspots and frontiers. Conclusions: Through this bibliometric research work, the research hotspot in the medical image segmentation field is uncovered and can point to future research in the field. It can be expected that more researchers will focus their work on deep learning neural network-based medical image segmentation.

Adversarial Learning for Effective Detector Training via Synthetic Data

Current neural network-based algorithms for object detection require a huge amount of training data. Creation and annotation of specific datasets for real-life applications require significant human and time resources that are not always available. This issue substantially prevents the successful deployment of AI algorithms in industrial tasks. One possible solutions is a synthesis of train images by rendering 3D models of target objects, which allows effortless automatic annotation. However, direct use of synthetic training datasets does not usually result in an increase of the algorithms’ quality on test data due to differences in data domains. In this paper, we propose the adversarial architecture and training method for a CNN-based detector, which allows the effective use of synthesized images in case of a lack of labeled real-world data. The method was successfully tested on real data and applied for the development of unmanned aerial vehicle (UAV) detection and localization system.

Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI.

Brain magnetic resonance imaging (MRI) is useful for predicting the outcome of patients with acute ischemic stroke (AIS). Although deep learning (DL) using brain MRI with certain image biomarkers has shown satisfactory results in predicting poor outcomes, no study has assessed the usefulness of natural language processing (NLP)-based machine learning (ML) algorithms using brain MRI free-text reports of AIS patients. Therefore, we aimed to assess whether NLP-based ML algorithms using brain MRI text reports could predict poor outcomes in AIS patients. This study included only English text reports of brain MRIs examined during admission of AIS patients. Poor outcome was defined as a modified Rankin Scale score of 3–6, and the data were captured by trained nurses and physicians. We only included MRI text report of the first MRI scan during the admission. The text dataset was randomly divided into a training and test dataset with a 7:3 ratio. Text was vectorized to word, sentence, and document levels. In the word level approach, which did not consider the sequence of words, and the “bag-of-words” model was used to reflect the number of repetitions of text token. The “sent2vec” method was used in the sensation-level approach considering the sequence of words, and the word embedding was used in the document level approach. In addition to conventional ML algorithms, DL algorithms such as the convolutional neural network (CNN), long short-term memory, and multilayer perceptron were used to predict poor outcomes using 5-fold cross-validation and grid search techniques. The performance of each ML classifier was compared with the area under the receiver operating characteristic (AUROC) curve. Among 1840 subjects with AIS, 645 patients (35.1%) had a poor outcome 3 months after the stroke onset. Random forest was the best classifier (0.782 of AUROC) using a word-level approach. Overall, the document-level approach exhibited better performance than did the word- or sentence-level approaches. Among all the ML classifiers, the multi-CNN algorithm demonstrated the best classification performance (0.805), followed by the CNN (0.799) algorithm. When predicting future clinical outcomes using NLP-based ML of radiology free-text reports of brain MRI, DL algorithms showed superior performance over the other ML algorithms. In particular, the prediction of poor outcomes in document-level NLP DL was improved more by multi-CNN and CNN than by recurrent neural network-based algorithms. NLP-based DL algorithms can be used as an important digital marker for unstructured electronic health record data DL prediction.

Multimodal Representation Learning and Set Attention for LWIR In-Scene Atmospheric Compensation

A multimodal generative modeling approach combined with permutation-invariant set attention is investigated in this article to support long-wave infrared (LWIR) in-scene atmospheric compensation. The generative model can produce realistic atmospheric state vectors (T, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O, O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ) and their corresponding transmittance, upwelling radiance, and downwelling radiance (TUD) vectors by sampling a low-dimensional space. Variational loss, LWIR radiative transfer loss, and atmospheric state loss constrain the low-dimensional space, resulting in lower reconstruction error compared to standard mean-squared error approaches. A permutation-invariant network predicts the generative model low-dimensional components from in-scene data, allowing for simultaneous estimates of the atmospheric state and TUD vector. Forward modeling the predicted atmospheric state vector results in a second atmospheric compensation estimate. Results are reported for collected LWIR data and compared against fast line-of-sight atmospheric analysis of hypercubes-infrared (FLAASHIR), demonstrating commensurate performance when applied to a target detection scenario. Additionally, an approximate eight times reduction in detection time is realized using this neural network-based algorithm compared to FLAASH-IR. Accelerating the target detection pipeline while providing multiple atmospheric estimates is necessary for many real world, time sensitive tasks.

Localisation algorithm for security access control in railway communications

With the dense deployment and wide applications of the Internet of Things in railway systems, the location-based security access control scheme is becoming increasingly important. In this study, the received signal strength (RSS) and channel state information (CSI) in railway communications are measured by AR9344 network interface card. Then, based on the measurement data, the authors propose trajectory-based, neural network-based (NN-based) and ray tracing-based (RT-based) localisation algorithms, serving for location-based security access control. Specifically, the trajectory-based algorithm combined with trajectory simulation, movement detection and dynamic time warping algorithms, realises passengers enter/exit pattern detection. The NN-based algorithm leverages back-propagation network (BPN) and constructs training sets with measurement-based RSS and CSI, finishing accurate localisation. Besides, they evaluate the algorithm performance under different layers of BPN. RT-based localisation algorithm combines measurement data and simulation analysis, leveraging simulated-based multiple-input multiple-output received power and delay spread to realise lightweight localisation. After evaluation, the RT-based algorithm can achieve the highest accuracy of localisation, up to 99.9% and is designed to be straightforward for integration with commercial access points and deployment to railway communications.

Assessment of Fatigue Using Wearable Sensors: A Pilot Study

Background: Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts patient health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are severely limited. Methods: After using a recurrent neural network-based algorithm to impute missing time series data form a multisensor wearable device, we compared supervised and unsupervised machine learning approaches to gain insights on the relationship between self-reported non-pathological fatigue and multimodal sensor data. Results: A total of 27 healthy subjects and 405 recording days were analyzed. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters, and daily questionnaires on fatigue. The best results were obtained when using the causal convolutional neural network model for unsupervised representation learning of multivariate sensor data, and random forest as a classifier trained on subject-reported physical fatigue labels (weighted precision of 0.70 ± 0.03 and recall of 0.73 ± 0.03). When using manually engineered features on sensor data to train our random forest (weighted precision of 0.70 ± 0.05 and recall of 0.72 ± 0.01), both physical activity (energy expenditure, activity counts, and steps) and vital signs (heart rate, heart rate variability, and respiratory rate) were important parameters to measure. Furthermore, vital signs contributed the most as top features for predicting mental fatigue compared to physical ones. These results support the idea that fatigue is a highly multimodal concept. Analysis of clusters from sensor data highlighted a digital phenotype indicating the presence of fatigue (95% of observations) characterized by a high intensity of physical activity. Mental fatigue followed similar trends but was less predictable. Potential future directions could focus on anomaly detection assuming longer individual monitoring periods. Conclusion: Taken together, these results are the first demonstration that multimodal digital data can be used to inform, quantify, and augment subjectively captured non-pathological fatigue measures.

A machine learning‐based, decision support, mobile phone application for diagnosis of common dermatological diseases

The integration of machine learning algorithms in decision support tools for physicians is gaining popularity. These tools can tackle the disparities in healthcare access as the technology can be implemented on smartphones. We present the first, large-scale study on patients with skin of colour, in which the feasibility of a novel mobile health application (mHealth app) wasinvestigated in actual clinical workflows. To develop a mHealth app to diagnose 40 common skin diseases and test it in clinical settings. A convolutional neural network-based algorithm was trained with clinical images of 40 skin diseases. A smartphone app was generated and validated on 5014 patients, attending rural and urban outpatient dermatology departments in India. The results of this mHealth app were compared against the dermatologists' diagnoses. The machine-learning model, in an in silico validation study, demonstrated an overall top-1 accuracy of 76.93±0.88% and mean area-under-curve of 0.95±0.02 on a set of clinical images. In the clinical study, on patients with skin of colour, the app achieved an overall top-1 accuracy of 75.07% (95% CI=73.75-76.36), top-3 accuracy of 89.62% (95% CI=88.67-90.52) and mean area-under-curve of 0.90±0.07. This study underscores the utility of artificial intelligence-driven smartphone applications as a point-of-care, clinical decision support tool for dermatological diagnosis for a wide spectrum of skin diseases in patients of the skin of colour.

Countering FDI Attacks on DERs Coordinated Control System Using FMI-Compatible Cosimulation

In this article, the resilience of a coordinated control system for a set of PV-based distributed energy resources (DERs) against false data injection (FDI) attacks is evaluated. The evaluation is performed using a functional mock-up interface (FMI)-compatible cosimulation platform which enables the interaction of multi-domain simulators (EMTP, MATLAB/Simulink, and NS-3). The cosimulation platform permits rigorous analysis of cybersecurity through detailed modeling of all system components. The DER coordinated control and communication systems implemented on the IEEE-34 bus benchmark consist of measurement, control and monitoring components including substation central controller, DER local controllers, synchrophasor network and advanced metering infrastructure (AMI). Some DERs are equipped with an energy storage system (ESS) and coordinated by the central control unit in order to correct voltage disturbances resulting from the intermittent solar photovoltaic (PV) generation. The FDI attack targets the AMI system and aims at manipulating the load profile messages reported by the smart meter collector, thus yielding a central control failure. To detect the attacks and mitigate their impacts, a neural network-based algorithm is proposed and incorporated in the central control unit. The effectiveness of the proposed detection and mitigation algorithm is confirmed through simulations using the proposed FMI-compatible cosimulation platform.

Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

Coal is heterogeneous in nature, and thus the characterization of coal is essential before its use for a specific purpose. Thus, the current study aims to develop a machine vision system for automated coal characterizations. The model was calibrated using 80 image samples that are captured for different coal samples in different angles. All the images were captured in RGB color space and converted into five other color spaces (HSI, CMYK, Lab, xyz, Gray) for feature extraction. The intensity component image of HSI color space was further transformed into four frequency components (discrete cosine transform, discrete wavelet transform, discrete Fourier transform, and Gabor filter) for the texture features extraction. A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development. The datasets of the optimized features were used as an input for the model, and their respective coal characteristics (analyzed in the laboratory) were used as outputs of the model. The R-squared values were found to be 0.89, 0.92, 0.92, and 0.84, respectively, for fixed carbon, ash content, volatile matter, and moisture content. The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression, support vector regression, and radial basis neural network models. The study demonstrates the potential of the machine vision system in automated coal characterization.