Data Clustering for Fuzzyfier Value Derivation

The fuzzifier value m is improving significant factor for achieving the accuracy of data. Therefore, in this chapter, various clustering method is introduced with the definition of important values for clustering. To adaptively calculate the appropriate purge value of the gap type −2 fuzzy c-means, two fuzzy values m1 and m2 are provided by extracting information from individual data points using a histogram scheme. Most of the clustering in this chapter automatically obtains determination of m1 and m2 values that depended on existent repeated experiments. Also, in order to increase efficiency on deriving valid fuzzifier value, we introduce the Interval type-2 possibilistic fuzzy C-means (IT2PFCM), as one of advanced fuzzy clustering method to classify a fixed pattern. In Efficient IT2PFCM method, proper fuzzifier values for each data is obtained from an algorithm including histogram analysis and Gaussian Curve Fitting method. Using the extracted information form fuzzifier values, two modified fuzzifier value m1 and m2 are determined. These updated fuzzifier values are used to calculated the new membership values. Determining these updated values improve not only the clustering accuracy rate of the measured sensor data, but also can be used without additional procedure such as data labeling. It is also efficient at monitoring numerous sensors, managing and verifying sensor data obtained in real time such as smart cities

Multi-objective function-based node-disjoint multipath routing for mobile ad hoc networks

Funding Information: This work was supported Korea Environmental Industry & Technology Institute (KEITI) grant funded by the Korea government (Ministry of Environment). Project No. RE202101551, the development of IoT-based technology for collecting and managing Big data on environmental hazards and health effects.Peer reviewedPublisher PD

CMOS Skin Sensor for Mobile Skin Diagnosis Using an Electronic Cotton Pad

This paper presents a complementary metal-oxide semiconductor (CMOS) skin sensor for detecting hydration, sebum, and ultraviolet (UV) protection. This sensor employs pixels comprising interdigitated capacitors (IDCs) for detecting hydration and a 30 x 24 photodiode (PD) array for detecting UV protection and sebum. The 4 x 8 pixels with IDCs over the PDs are used for area efficiency; they afford reliable detection regardless of the skin contact area and a high sensitivity, which is achieved via pixel merging. For the readout of both IDCs and PDs, a column-parallel multiple-sampling analog front-end and a 9b successive approximation register analog-to-digital converter are integrated. To detect UV protection under different wavelengths of UVA and UVB, we implement the spatiotemporal delta readout of the PDs. Furthermore, a fully characterized, proof-of-concept prototype chip is fabricated using a 110-nm CMOS process. Compared with conventional skin sensors, the proposed sensor exhibits higher sensitivities of 0.25%/min and 2.32%/mL in detecting dehydration rate and sebum levels, respectively. Moreover, the sensor can detect UV protection under UVA and UVB wavelengths. Owing to its core size of 2.32 x 4.65 mm(2), the proposed sensor can potentially be integrated into cotton pads for mobile skin diagnosis

The Seoul National University AGN Monitoring Project. IV. Hα Reverberation Mapping of Six AGNs and the Hα Size–Luminosity Relation

The broad-line region (BLR) size–luminosity relation has paramount importance for estimating the mass of black holes in active galactic nuclei (AGNs). Traditionally, the size of the Hβ BLR is often estimated from the optical continuum luminosity at 5100 Å, while the size of the Hα BLR and its correlation with the luminosity is much less constrained. As a part of the Seoul National University AGN Monitoring Project, which provides 6 yr photometric and spectroscopic monitoring data, we present our measurements of the Hα lags of high-luminosity AGNs. Combined with the measurements for 42 AGNs from the literature, we derive the size–luminosity relations of the Hα BLR against the broad Hα and 5100 Å continuum luminosities. We find the slope of the relations to be 0.61 ± 0.04 and 0.59 ± 0.04, respectively, which are consistent with the Hβ size–luminosity relation. Moreover, we find a linear relation between the 5100 Å continuum luminosity and the broad Hα luminosity across 7 orders of magnitude. Using these results, we propose a new virial mass estimator based on the Hα broad emission line, finding that the previous mass estimates based on scaling relations in the literature are overestimated by up to 0.7 dex at masses lower than 107M⊙

The Seoul National University AGN Monitoring Project IV: Hα\alpha reverberation mapping of 6 AGNs and the Hα\alpha Size-Luminosity Relation

The broad line region (BLR) size-luminosity relation has paramount importance for estimating the mass of black holes in active galactic nuclei (AGNs). Traditionally, the size of the H$\beta$ BLR is often estimated from the optical continuum luminosity at 5100\angstrom{} , while the size of the H$\alpha$ BLR and its correlation with the luminosity is much less constrained. As a part of the Seoul National University AGN Monitoring Project (SAMP) which provides six-year photometric and spectroscopic monitoring data, we present our measurements of the H$\alpha$ lags of 6 high-luminosity AGNs. Combined with the measurements for 42 AGNs from the literature, we derive the size-luminosity relations of H$\alpha$ BLR against broad H$\alpha$ and 5100\angstrom{} continuum luminosities. We find the slope of the relations to be $0.61\pm0.04$ and $0.59\pm0.04$, respectively, which are consistent with the \hb{} size-luminosity relation. Moreover, we find a linear relation between the 5100\angstrom{} continuum luminosity and the broad H$\alpha$ luminosity across 7 orders of magnitude. Using these results, we propose a new virial mass estimator based on the H$\alpha$ broad emission line, finding that the previous mass estimates based on the scaling relations in the literature are overestimated by up to 0.7 dex at masses lower than $10^7$~M$_{\odot}$.Comment: Accepted for publication in ApJ (Jun. 25th, 2023). 21 pages, 12 figure

Review of Internet of Things-Based Artificial Intelligence Analysis Method through Real-Time Indoor Air Quality and Health Effect Monitoring: Focusing on Indoor Air Pollution That Are Harmful to the Respiratory Organ

Everyone is aware that air and environmental pollutants are harmful to health. Among them, indoor air quality directly affects physical health, such as respiratory rather than outdoor air. However, studies that have examined the correlation between environmental and health information have been conducted with public data targeting large cohorts, and studies with real-time data analysis are insufficient. Therefore, this research explores the research with an indoor air quality monitoring (AQM) system based on developing environmental detection sensors and the internet of things to collect, monitor, and analyze environmental and health data from various data sources in real-time. It explores the usage of wearable devices for health monitoring systems. In addition, the availability of big data and artificial intelligence analysis and prediction has increased, investigating algorithmic studies for accurate prediction of hazardous environments and health impacts. Regarding health effects, techniques to prevent respiratory and related diseases were reviewed

Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2Seq

Owing to their wide application, Internet of Things systems have been the target of malicious attacks. These attacks included DoS, flood, SlowITe, malformed, and brute-force attacks. A dataset that includes these attacks was recently released. However, the attack detection accuracy reported in previous studies has not been satisfactory because the studies used too many features that are not important in detecting malicious message queue telemetry transport (MQTT) traffic. Therefore, this study aims to analyze these attacks. Herein, a novel feature extraction method is proposed that includes the source port index, TCP length, MQTT message type, keep alive, and connection acknowledgment. The attacks were classified using the Seq2Seq model. During the experiment, the accuracy of the proposed method was 99.97%, which is 7.33% higher than that of previously reported methods

Robust Anomaly Detection of Melt-Pool Monitoring for Laser Power Bed Additive Manufacturing Process

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Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2Seq

Owing to their wide application, Internet of Things systems have been the target of malicious attacks. These attacks included DoS, flood, SlowITe, malformed, and brute-force attacks. A dataset that includes these attacks was recently released. However, the attack detection accuracy reported in previous studies has not been satisfactory because the studies used too many features that are not important in detecting malicious message queue telemetry transport (MQTT) traffic. Therefore, this study aims to analyze these attacks. Herein, a novel feature extraction method is proposed that includes the source port index, TCP length, MQTT message type, keep alive, and connection acknowledgment. The attacks were classified using the Seq2Seq model. During the experiment, the accuracy of the proposed method was 99.97%, which is 7.33% higher than that of previously reported methods

Machine learning algorithms to predict the catalytic reduction performance of eco-toxic nitrophenols and azo dyes contaminants (Invited Article)

Removing hazardous substances like azo dyes and nitrophenols from drinking water is essential for maintaining human health since these substances occur naturally in the environment. This research study used machine learning techniques to estimate the catalytic reduction performance of environmentally hazardous nitrophenols and azodyes pollutants. The catalyst PdO-NiO was used to eliminate contaminants in the water, including 4-nitrophenol (4-NP), 2,4-dinitrophenol (DNP), 2,4,6-trinitrophenol (TNP), methylene blue (MB), Rhodamine B (RHB), and Methyl Orange (MO). We conducted the experiments at different timings, and machine learning algorithms, including Linear Regression (LR), Support Vector Machines (SVM), Gradient boosted machines (GBM), Random forest (RF), and XGBTree (XGB), were used to predict the catalytic activity. The performance of these algorithms was measured using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results showed that the XGB algorithm performs best with NP and DNP. RF algorithm performs best with TNP, MB, and RHB, and the SVM algorithm performs best with MO. PdO-NiO bimetallic catalyst showed 98% reduction efficiency of azo compounds mixture within 8 min. Hence, we found PdO-NiO to be an efficient catalyst for real-site applications