scholarly journals A Novel Detection Performance Modular Evaluation Metric of Space-based Infrared System

2021 ◽  
Author(s):  
Xiaoxuan Zhou ◽  
Xinyue Ni ◽  
Jingwen Zhang ◽  
Dongshan Weng ◽  
Zhuoyue Hu ◽  
...  
Keyword(s):  
Detection System ◽  
Detection Performance ◽  
Parameter Design ◽  
Infrared Detection ◽  
System Parameters ◽  
Detection Model ◽  
Fifth Generation ◽  
The Earth ◽  
Evaluation Metric ◽  
The Impact

Abstract In order to reflect the space-based full chain information of the detection process comprehensively and objectively, we proposed a novel modular evaluation metric to discuss the target, background and system independently. It takes the equivalent radiation intensity as the parameter, which can evaluate the detection performance of the system quantitatively. In this paper, taking the fifth-generation American stealth fighter F22 as an example, the mathematical detection model of the space-based infrared system to aircraft targets in the Earth background is described. A modular evaluation metric is proposed. The simulation analyzes the impact of different detection scenes and system parameters on system equivalent irradiance. Furthermore, recommendations for the optimization of the detection system are given. The research results provide a new idea for the analysis of the detection performance of highly maneuverable targets under dynamic backgrounds and have guiding significance for the performance evaluation and parameter design of the infrared detection system.

scholarly journals Impact of Unreliable Content on Social Media Users during COVID-19 and Stance Detection System

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 5
Author(s):  
Mudasir Ahmad Wani ◽  
Nancy Agarwal ◽  
Patrick Bours
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Detection System ◽  
Online Social Network ◽  
Negative Attitude ◽  
Health Crisis ◽  
Online Groups ◽  
Detection Model ◽  
Twitter Users ◽  
The Impact

The abundant dissemination of misinformation regarding coronavirus disease 2019 (COVID-19) presents another unprecedented issue to the world, along with the health crisis. Online social network (OSN) platforms intensify this problem by allowing their users to easily distort and fabricate the information and disseminate it farther and rapidly. In this paper, we study the impact of misinformation associated with a religious inflection on the psychology and behavior of the OSN users. The article presents a detailed study to understand the reaction of social media users when exposed to unverified content related to the Islamic community during the COVID-19 lockdown period in India. The analysis was carried out on Twitter users where the data were collected using three scraping packages, Tweepy, Selenium, and Beautiful Soup, to cover more users affected by this misinformation. A labeled dataset is prepared where each tweet is assigned one of the four reaction polarities, namely, E (endorse), D (deny), Q (question), and N (neutral). Analysis of collected data was carried out in five phases where we investigate the engagement of E, D, Q, and N users, tone of the tweets, and the consequence upon repeated exposure of such information. The evidence demonstrates that the circulation of such content during the pandemic and lockdown phase had made people more vulnerable in perceiving the unreliable tweets as fact. It was also observed that people absorbed the negativity of the online content, which induced a feeling of hatred, anger, distress, and fear among them. People with similar mindset form online groups and express their negative attitude to other groups based on their opinions, indicating the strong signals of social unrest and public tensions in society. The paper also presents a deep learning-based stance detection model as one of the automated mechanisms for tracking the news on Twitter as being potentially false. Stance classifier aims to predict the attitude of a tweet towards a news headline and thereby assists in determining the veracity of news by monitoring the distribution of different reactions of the users towards it. The proposed model, employing deep learning (convolutional neural network(CNN)) and sentence embedding (bidirectional encoder representations from transformers(BERT)) techniques, outperforms the existing systems. The performance is evaluated on the benchmark SemEval stance dataset. Furthermore, a newly annotated dataset is prepared and released with this study to help the research of this domain.

scholarly journals Nephrotoxin-Related Acute Kidney Injury and Predicting High-Risk Medication Combinations in the Hospitalized Child

2019 ◽  
Vol 14 (8) ◽  
pp. 462-467
Author(s):  
Karyn E Yonekawa ◽  
Chuan Zhou ◽  
Wren L Haaland ◽  
Davene R Wright
Keyword(s):  
Acute Kidney Injury ◽  
Decision Support ◽  
High Risk ◽  
Detection System ◽  
Kidney Injury ◽  
Clinical Decision ◽  
Detection Sensitivity ◽  
Detection Model ◽  
Improved Model ◽  
The Impact

BACKGROUND: In the hospitalized patient, nephrotoxin exposure is one potentially modifiable risk factor for acute kidney injury (AKI). Clinical decision support based on nephrotoxin ordering was developed at our hospital to assist inpatient providers with the prevention or mitigation of nephrotoxin-related AKI. The initial decision support algorithm (Algorithm 1) was modified in order to align with a national AKI collaborative (Algorithm 2). OBJECTIVE: Our first aim was to determine the impact of this alignment on the sensitivity and specificity of our nephrotoxin-related AKI detection system. Second, if the system efficacy was found to be suboptimal, we then sought to develop an improved model. DESIGN: A retrospective cohort study in hospitalized patients between December 1, 2013 and November 30, 2015 (N = 14,779) was conducted. INTERVENTIONS: With the goal of increasing nephrotoxin-related AKI detection sensitivity, a novel model based on the identification of combinations of high-risk medications was developed. RESULTS: Application of the algorithms to our nephrotoxin use and AKI data resulted in sensitivities of 46.9% (Algorithm 1) and 43.3% (Algorithm 2, P = .22) and specificities of 73.6% and 89.3%, respectively (P < .001). Our novel AKI detection model was able to deliver a sensitivity of 74% and a specificity of 70%. CONCLUSIONS: Modifications to our AKI detection system by adopting Algorithm 2, which included an expanded list of nephrotoxins and equally weighting each medication, did not improve our nephrotoxin-related AKI detection. It did improve our system’s specificity. Sensitivity increased by >50% when we applied a novel algorithm based on observed data with identification of key medication combinations.

scholarly journals Detecting Temporal Attacks: An Intrusion Detection System for Train Communication Ethernet Based on Dynamic Temporal Convolutional Network

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Chuan Yue ◽  
Lide Wang ◽  
Dengrui Wang ◽  
Ruifeng Duo ◽  
Haipeng Yan
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Detection Performance ◽  
Second Phase ◽  
Convolutional Network ◽  
Detection Model ◽  
Systematic Feature ◽  
Two Phases

The train communication Ethernet (TCE) of modern intelligent trains is under an ever-increasing threat of serious network attacks. Denial of service (DoS) and man in the middle (MITM), the two most destructive attacks against TCE, are difficult to detect by conventional methods. Aiming at their highly time-correlated properties, a novel dynamic temporal convolutional network-based intrusion detection system (DyTCN-IDS) is proposed in this paper to detect these temporal attacks. A semiphysical TCE testbed that is capable of simulating real situations in TCE-based trains is first built to generate an effective dataset for training and testing. DyTCN-IDS consists of two phases, and in the first phase, systematic feature engineering is designed to optimize the dataset. In the second phase, a basic detection model that is good at dealing with temporal features is first built by utilizing the temporal convolutional network with several architectural optimizations. Then, in order to decrease the computational consumption waste on network packet sequences with different lengths of inner temporal relationships, dynamic neural network technology is further adopted to optimize the basic detection model. Diverse experiments were carried out to evaluate the proposed system from different angles. The experimental results indicate that our system is easy to train, converges fast, costs less computational resources, and achieves satisfying detection performance with a macro false alarm rate of 0.09%, a macro F-score of 99.39%, and an accuracy of 99.40%. Compared to some canonical DL-based IDS and some latest IDS, our system acquires the best overall detection performance as well.

scholarly journals Deepfake detection by human crowds, machines, and machine-informed crowds

2021 ◽  
Vol 119 (1) ◽  
pp. e2110013119
Author(s):  
Matthew Groh ◽  
Ziv Epstein ◽  
Chaz Firestone ◽  
Rosalind Picard
Keyword(s):  
Computer Vision ◽  
Visual Processing ◽  
Detection Performance ◽  
Cognitive Capacities ◽  
Detection Model ◽  
Machine Performance ◽  
Recent Emergence ◽  
Model Predictions ◽  
Human Participants ◽  
The Impact

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance.

Formation of Lunar Craters and Bright Rays as a Result of Meteorite Impacts

1962 ◽  
Vol 14 ◽  
pp. 415-418
Author(s):  
K. P. Stanyukovich ◽  
V. A. Bronshten
Keyword(s):  
Explosive Charge ◽  
The Moon ◽  
Meteorite Impacts ◽  
The Earth ◽  
Lunar Craters ◽  
The Impact

The phenomena accompanying the impact of large meteorites on the surface of the Moon or of the Earth can be examined on the basis of the theory of explosive phenomena if we assume that, instead of an exploding meteorite moving inside the rock, we have an explosive charge (equivalent in energy), situated at a certain distance under the surface.

scholarly journals DNA tetrahedron-mediated immune-sandwich assay for rapid and sensitive detection of PSA through a microfluidic electrochemical detection system

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Dezhi Feng ◽  
Jing Su ◽  
Yi Xu ◽  
Guifang He ◽  
Chenguang Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Reaction Time ◽  
Electrochemical Detection ◽  
Detection System ◽  
Detection Performance ◽  
Pdms Layer ◽  
Microfluidic Chips ◽  
Serum Samples ◽  
Gold Nanoflowers ◽  
Dna Tetrahedron

AbstractProstate-specific antigen (PSA) is the most widely used biomarker for the early diagnosis of prostate cancer. Existing methods for PSA detection are burdened with some limitations and require improvement. Herein, we developed a novel microfluidic–electrochemical (μFEC) detection system for PSA detection. First, we constructed an electrochemical biosensor based on screen-printed electrodes (SPEs) with modification of gold nanoflowers (Au NFs) and DNA tetrahedron structural probes (TSPs), which showed great detection performance. Second, we fabricated microfluidic chips by DNA TSP-Au NF-modified SPEs and a PDMS layer with designed dense meandering microchannels. Finally, the μFEC detection system was achieved based on microfluidic chips integrated with the liquid automatic conveying unit and electrochemical detection platform. The μFEC system we developed acquired great detection performance for PSA detection in PBS solution. For PSA assays in spiked serum samples of the μFEC system, we obtained a linear dynamic range of 1–100 ng/mL with a limit of detection of 0.2 ng/mL and a total reaction time <25 min. Real serum samples of prostate cancer patients presented a strong correlation between the “gold-standard” chemiluminescence assays and the μFEC system. In terms of operation procedure, cost, and reaction time, our method was superior to the current methods for PSA detection and shows great potential for practical clinical application in the future.

scholarly journals Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2538
Author(s):  
Shuang Zhang ◽  
Feng Liu ◽  
Yuang Huang ◽  
Xuedong Meng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spread Spectrum ◽  
High Efficiency ◽  
Detection System ◽  
Direct Sequence Spread Spectrum ◽  
Detection Performance ◽  
Detection Methods ◽  
Direct Sequence ◽  
Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

scholarly journals Impact of the Generation System Parameters on the Frequency Response of the Power System: A UK Grid Case Study

Electricity ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 143-157
Author(s):  
Jovi Atkinson ◽  
Ibrahim M. Albayati
Keyword(s):  
Power System ◽  
Frequency Response ◽  
Power Grid ◽  
Sudden Increase ◽  
Generation System ◽  
System Parameters ◽  
Primary Frequency ◽  
Primary Frequency Response ◽  
The Impact ◽  
System Inertia

The operation and the development of power system networks introduce new types of stability problems. The effect of the power generation and consumption on the frequency of the power system can be described as a demand/generation imbalance resulting from a sudden increase/decrease in the demand and/or generation. This paper investigates the impact of a loss of generation on the transient behaviour of the power grid frequency. A simplified power system model is proposed to examine the impact of change of the main generation system parameters (system inertia, governor droop setting, load damping constant, and the high-pressure steam turbine power fraction), on the primary frequency response in responding to the disturbance of a 1.32 GW generation loss on the UK power grid. Various rates of primary frequency responses are simulated via adjusting system parameters of the synchronous generators to enable the controlled generators providing a fast-reliable primary frequency response within 10 s after a loss of generation. It is concluded that a generation system inertia and a governor droop setting are the most dominant parameters that effect the system frequency response after a loss of generation. Therefore, for different levels of generation loss, the recovery rate will be dependent on the changes of the governor droop setting values. The proposed model offers a fundamental basis for a further investigation to be carried on how a power system will react during a secondary frequency response.

scholarly journals Measuring Key Quality Indicators in Cloud Gaming: Framework and Assessment Over Wireless Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1387
Author(s):  
Oswaldo Sebastian Peñaherrera-Pulla ◽  
Carlos Baena ◽  
Sergio Fortes ◽  
Eduardo Baena ◽  
Raquel Barco
Keyword(s):  
Communication Networks ◽  
Mobile Networks ◽  
Video Game ◽  
Know How ◽  
Fifth Generation ◽  
New Generation ◽  
Graphics Rendering ◽  
The Impact

Cloud Gaming is a cutting-edge paradigm in the video game provision where the graphics rendering and logic are computed in the cloud. This allows a user’s thin client systems with much more limited capabilities to offer a comparable experience with traditional local and online gaming but using reduced hardware requirements. In contrast, this approach stresses the communication networks between the client and the cloud. In this context, it is necessary to know how to configure the network in order to provide service with the best quality. To that end, the present work defines a novel framework for Cloud Gaming performance evaluation. This system is implemented in a real testbed and evaluates the Cloud Gaming approach for different transport networks (Ethernet, WiFi, and LTE (Long Term Evolution)) and scenarios, automating the acquisition of the gaming metrics. From this, the impact on the overall gaming experience is analyzed identifying the main parameters involved in its performance. Hence, the future lines for Cloud Gaming QoE-based (Quality of Experience) optimization are established, this way being of configuration, a trendy paradigm in the new-generation networks, such as 4G and 5G (Fourth and Fifth Generation of Mobile Networks).

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