Effect of Proximity, Burden, and Position on the Power Quality Accuracy Performance of Rogowski Coils

Power quality evaluation is the process of assessing the actual power network parameters with respect to the ideal conditions. However, several new assets and devices among the grid include mining the voltage and current quality. For example, the power converters needed for renewable energy sources’ connection to the grid, electric vehicles, etc., are some of the main sources of disturbances that inject high-frequency components into the grid. Consequently, instrument transformers (ITs) should be capable of measuring distorted currents and voltages with the same level of accuracy guaranteed for the ideal frequency (50–60 Hz). This is not a simple task if one considers that several other influence quantities endlessly act on the ITs. To this purpose, considering the lack of a standard, this work presents a measurement setup and specific tests for testing a commonly used type of low-power current transformer, the Rogowski coil (RC). In particular, the accuracy performance (ratio error and phase displacement) of the RCs was evaluated when measuring distorted signals while other influence quantities affected the RCs. Such quantities included positioning, burden, and magnetic field. The results indicate which quantities (or combination of them) have the greatest effect on the RC’s accuracy performance.

On the Performance of Generative Adversarial Network by Limiting Mode Collapse for Malware Detection Systems

Generative adversarial network (GAN) has been regarded as a promising solution to many machine learning problems, and it comprises of a generator and discriminator, determining patterns and anomalies in the input data. However, GANs have several common failure modes. Typically, a mode collapse occurs when a GAN fails to fit the set optimizations and leads to several instabilities in the generative model, diminishing the capability to generate new content regardless of the dataset. In this paper, we study conditional limiter solutions for mode collapse for the Intrusion Detection System (IDS) Control Flow GAN (ICF-GAN) model. Specifically, the ICF-GAN’s mode collapse instances are limited by a mini-batch method that significantly improves the model accuracy. Performance evaluation is conducted using numerical results obtained from experiments.

Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks

Singing voice detection is still a challenging task because the voice can be obscured by instruments having the same frequency band, and even the same timbre, produced by mimicking the mechanism of human singing. Because of the poor adaptability and complexity of feature engineering, there is a recent trend towards feature learning in which deep neural networks play the roles of feature extraction and classification. In this paper, we present two methods to explore the channel properties in the convolution neural network to improve the performance of singing voice detection by feature learning. First, channel attention learning is presented to measure the importance of a feature, in which two attention mechanisms are exploited, i.e., the scaled dot-product and squeeze-and-excitation. This method focuses on learning the importance of the feature map so that the neurons can place more attention on the more important feature maps. Second, the multi-scale representations are fed to the input channels, aiming at adding more information in terms of scale. Generally, different songs need different scales of a spectrogram to be represented, and multi-scale representations ensure the network can choose the best one for the task. In the experimental stage, we proved the effectiveness of the two methods based on three public datasets, with the accuracy performance increasing by up to 2.13 percent compared to its already high initial level.

The pneumonia severity index: assessment and comparison to popular machine learning classifiers

Pneumonia is the top communicable cause of death worldwide. Accurate prognostication of patient severity with Community Acquired Pneumonia (CAP) allows better patient care and hospital management. The Pneumonia Severity Index (PSI) was developed in 1997 as a tool to guide clinical practice by stratifying the severity of patients with CAP. While the PSI has been evaluated against other clinical stratification tools, it has not been evaluated against multiple classic machine learning classifiers in various metrics over large sample size. In this paper, we evaluated and compared the prediction performance of nine classic machine learning classifiers with PSI over 34720 adult (age 18+) patient records collected from 749 hospitals from 2009 to 2018 in the United States on Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and Average Precision (Precision-Recall AUC). Machine learning classifiers, such as Random Forest, provided a significant improvement (∼29% in PR AUC and ∼5% in ROC AUC) compared to PSI and required only 7 input values (compared to 20 parameters used in PSI). There were also statistically significant differences (p<0.05) between Random Forest and PSI among various races/ethnicities. Because of its ease of use, PSI remains a very strong clinical decision tool, but machine learning classifiers can provide better prediction accuracy performance. Comparing prediction performance across multiple metrics such as PR AUC, instead of ROC AUC alone can provide additional insight.Key MessagesThis work compared the prognostication accuracy performance of patient severity with Community Acquired Pneumonia (CAP) between Pneumonia Severity Index (PSI) and nine machine learning classifiers and found machine learning classifiers provided a significant improvement.

Visual Entity Linking via Multi-modal Learning

Existing visual scene understanding methods mainly focus on identifying coarse-grained concepts about the visual objects and their relationships, largely neglecting fine-grained scene understanding. In fact, many data-driven applications on the web (e.g. newsreading and e-shopping) require to accurately recognize much less coarse concepts as entities and properly link to a knowledge graph, which can take their performance to the next level. In light of this, in this paper, we identify a new research task: visual entity linking for fine-grained scene understanding. To accomplish the task, we first extract features of candidate entities from different modalities, i.e., visual features, textual features, and KG features. Then, we design a deep modal-attention neural network-based learning-to-rank method aggregates all features and map visual objects to the entities in KG. Extensive experimental results on the newly constructed dataset show that our proposed method is effective as it significantly improves the accuracy performance from 66.46% to 83.16% comparing with baselines.

Return of the Righting Reflex Does Not Portend Recovery of Cognitive Function in Anesthetized Rats

As the number of individuals undergoing general anesthesia rises globally, it becomes increasingly important to understand how consciousness and cognition are restored after anesthesia. In rodents, levels of consciousness are traditionally captured by physiological responses such as the return of righting reflex (RORR). However, tracking the recovery of cognitive function is comparatively difficult. Here we use an operant conditioning task, the 5-choice serial reaction time task (5-CSRTT), to measure sustained attention, working memory, and inhibitory control in male and female rats as they recover from the effects of several different clinical anesthetics. In the 5-CSRTT, rats learn to attend to a five-windowed touchscreen for the presentation of a stimulus. Rats are rewarded with food pellets for selecting the correct window within the time limit. During each session we tracked both the proportion of correct (accuracy) and missed (omissions) responses over time. Cognitive recovery trajectories were assessed after isoflurane (2% for 1 h), sevoflurane (3% for 20 min), propofol (10 mg/kg I.V. bolus), ketamine (50 mg/kg I.V. infusion over 10 min), and dexmedetomidine (20 and 35 μg/kg I.V. infusions over 10 min) for up to 3 h following RORR. Rats were classified as having recovered accuracy performance when four of their last five responses were correct, and as having recovered low omission performance when they missed one or fewer of their last five trials. Following isoflurane, sevoflurane, and propofol anesthesia, the majority (63–88%) of rats recovered both accuracy and low omission performance within an hour of RORR. Following ketamine, accuracy performance recovers within 2 h in most (63%) rats, but low omission performance recovers in only a minority (32%) of rats within 3 h. Finally, following either high or low doses of dexmedetomidine, few rats (25–32%) recover accuracy performance, and even fewer (0–13%) recover low omission performance within 3 h. Regardless of the anesthetic, RORR latency is not correlated with 5-CSRTT performance, which suggests that recovery of neurocognitive function cannot be inferred from changes in levels of consciousness. These results demonstrate how operant conditioning tasks can be used to assess real-time recovery of neurocognitive function following different anesthetic regimens.

Evaluation of Physics Frameworks for Building Web-Based Games

Recently, WebGL technology has shown a lot of potential for developing games. Since this technology is still relatively new, there is still much potential in the game development area that has not been explored yet. This paper explores the development of a game engine made with WebGL technology that integrates some physics frameworks for developing web-based 2D or 3D games. Specifically, we integrated three open source physics frameworks, which are Bullet, Cannon, and JigLib, into a WebGL-based game engine. We assessed these frameworks using some experiments, in terms of their correctness or accuracy, performance, completeness and compatibility. The results show that it is possible to integrate open source physics frameworks into a WebGL-based game engine, and Bullet is the best physics framework to be integrated into a WebGL-based game engine.

Person authentication based on eye-closed and visual stimulation using EEG signals

The study of Electroencephalogram (EEG)-based biometric has gained the attention of researchers due to the neurons’ unique electrical activity representation of an individual. However, the practical application of EEG-based biometrics is not currently widespread and there are some challenges to its implementation. Nowadays, the evaluation of a biometric system is user driven. Usability is one of the concerning issues that determine the success of the system. The basic elements of the usability of a biometric system are effectiveness, efficiency and user satisfaction. Apart from the mandatory consideration of the biometric system’s performance, users also need an easy-to-use and easy-to-learn authentication system. Thus, to satisfy these user requirements, this paper proposes a reasonable acquisition period and employs a consumer-grade EEG device to authenticate an individual to identify the performances of two acquisition protocols: eyes-closed (EC) and visual stimulation. A self-collected database of eight subjects was utilized in the analysis. The recording process was divided into two sessions, which were the morning and afternoon sessions. In each session, the subject was requested to perform two different tasks: EC and visual stimulation. The pairwise correlation of the preprocessed EEG signals of each electrode channel was determined and a feature vector was formed. Support vector machine (SVM) was then used for classification purposes. In the performance analysis, promising results were obtained, where EC protocol achieved an accuracy performance of 83.70–96.42% while visual stimulation protocol attained an accuracy performance of 87.64–99.06%. These results have demonstrated the feasibility and reliability of our acquisition protocols with consumer-grade EEG devices.

Performance of Various Deep-Learning Networks in the Seed Classification Problem

We report the results of an in-depth study of 15 variants of five different Convolutional Neural Network (CNN) architectures for the classification of seeds of seven different grass species that possess symmetry properties. The performance metrics of the nets are investigated in relation to the computational load and the number of parameters. The results indicate that the relation between the accuracy performance and operation count or number of parameters is linear in the same family of nets but that there is no relation between the two when comparing different CNN architectures. Using default pre-trained weights of the CNNs was found to increase the classification accuracy by ≈3% compared with training from scratch. The best performing CNN was found to be DenseNet201 with a 99.42% test accuracy for the highest resolution image set.

OPTIMAL ACCURACY PERFORMANCE IN MUSIC-BASED EEG SIGNAL USING MATTHEW CORRELATION COEFFICIENT ADVANCED (MCCA)

The connection between music and human are very synonyms because music could reduce stress. The state of stress could be measured using EEG signal, an electroencephalogram (EEG) measurement which contains an arousal and valence index value. In previous studies, it is found that the Matthew Correlation Coefficient (MCC) performance accuracy is of 85±5%. The arousal indicates strong emotion, and valence indicates positive and negative degree of emotion. Arousal and valence values could be used to measure the accuracy performance. This research focuses on the enhance MCC parameter equation based on arousal and valence values to perform the maximum accuracy percentage in the frequency domain and time-frequency domain analysis. Twenty-one features were used to improve the significance of feature extraction results and the investigated arousal and valence value. The substantial feature extraction involved alpha, beta, delta and theta frequency bands in measuring the arousal and valence index formula. Based on the results, the arousal and valance index is accepted to be applied as parameters in the MCC equations. However, in certain cases, the improvement of the MCC parameter is required to achieve a high accuracy percentage and this research proposed Matthew correlation coefficient advanced (MCCA) in order to improve the performance result by using a six sigma method. In conclusion, the MCCA equation is established to enhance the existing MCC parameter to improve the accuracy percentage up to 99.9% for the arousal and valence index.