Threshold-based Methods Research Articles

An Enhanced Segmentation Method Using a Fuzzy Clustering Technique for Colored Satellite Images

Aim: Satellite images are significantly more accessible to collect and include a huge amount of informative data in selected geographical areas. However, because of their vast dimensions and acquisition procedures, information extraction or segmentation is an extremely complicated procedure. So, this paper proposes a satellite image segmentation technique to extract required information that can be applied to real-time applications. Background: Satellite images are vast sources of information that help to perceive the earth’s surface and relevant changes in it. In satellite imaging, image segmentation is crucial as it leads to better classification and understanding of the data present in the considered images. Objective: This paper presents an enhanced segmentation technique based on color-based fuzzy cmean clustering (FCM) presents an improved segmentation technique based on color-based fuzzy c-mean clustering. Method: One of the popular types of soft clustering techniques that is utilized for image segmentation is fuzzy c-mean clustering. It is chosen for its robust features in data categorization. This study suggests an FCM method for segmenting colored satellite images based on clusters created using the colors red, green, and blue. Result: The performance of the proposed system is done with seven test images by comparing the segmented output of each image obtained by the popular threshold technique and the proposed methodology. Four performance metrics are employed in quantitative analysis to assess the effectiveness of the proposed method: entropy, standard deviation (SD), PIQE (Perception Image Quality Evaluator), and NIQE (Naturalness Image Quality Evaluator). A higher value of Entropy denotes better quality of images and a lower value of NIQE shows more intricate image details. In both the parameters, the images obtained by the proposed techniques showed better quality as per an increase in their entropy value and a decrease in NIQE value. Conclusion: The traditional threshold-based methods are applied to assess the performance of the proposed methodology utilizing four image-measuring parameters: entropy, NIQE, PIQE, and standard deviation. Overall, better results are obtained in all test cases using the proposed FCMbased clustering technique.Applying qualitative as well as quantitative analysis, the proposed method has compared the performance of the threshold technique with the proposed approach using seven satellite images. Experimental images taken from the public domain.

Dynamic Road Anomaly Detection: Harnessing Smartphone Accelerometer Data with Incremental Concept Drift Detection and Classification.

Effective monitoring of road conditions is crucial for ensuring safe and efficient transportation systems. By leveraging the power of crowd-sourced smartphone sensor data, road condition monitoring can be conducted in real-time, providing valuable insights for transportation planners, policymakers, and the general public. Previous studies have primarily focused on the use of pre-trained machine learning models and threshold-based methods for anomaly classification, which may not be suitable for real-world scenarios that require incremental detection and classification. As a result, there is a need for novel approaches that can adapt to changing data environments and perform effective classification without relying on pre-existing training data. This study introduces a novel, real-time road condition monitoring technique harnessing smartphone sensor data, addressing the limitations of pre-trained models that lack adaptability in dynamic environments. A hybrid anomaly detection method, combining unsupervised and supervised learning, is proposed to effectively manage concept drift, demonstrating a significant improvement in accuracy and robustness with a 96% success rate. The findings underscore the potential of incremental learning to enhance model responsiveness and efficiency in distinguishing various road anomalies, offering a promising direction for future transportation safety and resource optimization strategies.

Evaluation and Anomaly Detection Methods for Broadcast Ephemeris Time Series in the BeiDou Navigation Satellite System.

Broadcast ephemeris data are essential for the precision and reliability of the BeiDou Navigation Satellite System (BDS) but are highly susceptible to anomalies caused by various interference factors, such as ionospheric and tropospheric effects, solar radiation pressure, and satellite clock biases. Traditional threshold-based methods and manual review processes are often insufficient for detecting these complex anomalies, especially considering the distinct characteristics of different satellite types. To address these limitations, this study proposes an automated anomaly detection method using the IF-TEA-LSTM model. By transforming broadcast ephemeris data into multivariate time series and integrating anomaly score sequences, the model enhances detection robustness through data integrity assessments and stationarity tests. Evaluation results show that the IF-TEA-LSTM model reduces the RMSE by up to 20.80% for orbital parameters and improves clock deviation prediction accuracy for MEO satellites by 68.37% in short-term forecasts, outperforming baseline models. This method significantly enhances anomaly detection accuracy across GEO, IGSO, and MEO satellite orbits, demonstrating its superiority in long-term data processing and its capacity to improve the reliability of satellite operations within the BDS.

A Comparative Analysis of Swarm Intelligence Algorithms for Event Detection in Wireless Sensor Networks

WSNs play an important role in monitoring and responding to dynamic events in applications such as disaster management, industrial automation, and environmental monitoring. Since event detection has the highest priority in a WSN, it is very crucial regarding timely response, energy efficiency, and system reliability. However, traditional threshold-based detection methods often lack flexibility and always consume large amounts of energy. SI algorithms, motivated by natural collective behaviors, offer robust and adaptive solutions for event detection in order to overcome the limitations of conventional methods. Performance analysis and comparison of three most powerful SI algorithms - DA, ACO, and PSO - for event detection in WSNs. Methods: The performance of these algorithms was analyzed for time efficiency, energy consumption, accuracy, and scalability using simulation-based experiments. The experiments simulate WSNs of different node densities and event characteristics on the widely used python and MATLAB platforms under identical conditions for each algorithm. Among them, the Dragonfly Algorithm outperformed ACO and PSO in time efficiency-66.48 seconds versus 85.10 and 77.03 seconds, respectively-energy consumption (0.000030 J vs. 0.000047 and 0.000035 J), and accuracy, 94.5% versus 92.1% and 93.2%, correspondingly. DA also has better scalability for different network sizes and performs consistently well for larger networks, up to 1024 nodes. ACO, though robust in path exploration, had the highest energy consumption and longest detection time, which seriously affects its scalability. PSO gave balanced performance, though it trailed DA significantly in key metrics. The Dragonfly Algorithm provides the most efficient and scalable solution for WSN event detection with high time efficiency, energy conservation, and accuracy. These results underline its applicability in real-world time-critical and energy-sensitive WSN applications.

Cardiovascular magnetic resonance semi-automated threshold-based post-processing of right ventricular volumes in repaired tetralogy of Fallot.

Cardiovascular magnetic resonance (CMR) is the gold-standard to estimate right ventricular (RV) volumes, which are key for clinical management of patients with repaired tetralogy of Fallot (rTOF). Semi-automated threshold-based methods (SAT) have been proposed for CMR post-processing as alternatives to fully manual standard tracing. We investigated the impact of SAT on RV analysis using different thresholds in rTOF patients. RV volumes and mass were estimated using SAT and standard fully manual tracing methods in rTOF patients. Two threshold levels were set for SAT, i.e., default 50 (SAT-50) and 30 (SAT-30). RV stroke volumes (SV) were compared to main pulmonary artery forward flow (MPA-FF). Post-processing time, intra- and interobserver variabilities were compared across methods. Sixty-two CMRs of rTOF patients were analyzed. Compared to the standard fully manual tracing, no significant differences in RV mass, volumes and ejection fraction were observed using SAT-30, whereas SAT-50 significantly underestimated RV end-diastolic-volume index (EDVi) by 10.4% (mean difference of - 11.8 ± 6.2ml/m2, p 0.03) and overestimated RV mass index by 21.8% (mean difference of 14.2 ± 11.9g/m2, p 0.002). Compared to MPA-FF, RVSV by standardfully manual method and SAT-30 showed minor biases, respectively, 0.03ml/m2 and 0.7ml/m2, while SAT-50 underestimated RVSV by 6.86ml/m2 (p < 0.001). In six patients, the degree of RV EDVi underestimation by SAT-50 determined a change of category from dilated to non-dilated RV. Intra- and interobserver variabilities were good to excellent for all methods. Post-processing duration was shorter for SAT compared to standardmanual segmentation (5.5 ± 1.7min vs. 19.5 ± 4.4min, p < 0.001). CMR SAT-30 post-processing is a precise, accurate and time-saving method for biventricular assessment of volumes, ejection fraction and mass in rTOF.

Advancing lung ultrasound imaging: advanced pre-processing and neoteric segmentation for improved accuracy

Abstract Within the medical imaging domain, the segmentation of Lung Ultrasound (LUS) images is crucial for accurately defining regions of interest, particularly for lung health assessments. various techniques such as threshold-based, region-based, and edge-based methods have been employed to address this challenge, their accuracy and efficiency are often questioned. This highlights the need for an innovative approach. With this context in mind, this research introduces a novel preprocessing step using Optimized Gaussian Histogram Equalization (GHE) followed by a Neoteric Segmentation (NS) approach. This combination enhances the capability of segmenting specific regions within LUS images, regardless of the overarching medical scenario. The primary focus of this method is to understand, through a thorough evaluation, how neoteric segmentation compares with more conventional methods. By conducting an in-depth comparative analysis, valuable insights can be unearthed. The results demonstrate that the optimized GHE technique significantly enhances contrast and preserves visual quality, while the NS approach shows superior performance in accurately segmenting the lung regions. Initial findings suggest that the NS method, achieving an impressive 95% accuracy, holds significant promise and could influence the trajectory of healthcare practices. This research aims to advance the field of medical image analysis by highlighting the potential benefits of this cutting-edge segmentation technique. These advancements could positively impact healthcare outcomes, especially in contexts that leverage lung ultrasound imaging.

AI-Driven Anomaly Detection in Network Monitoring Techniques and Tools

Effective network monitoring is crucial for maintaining performance and security. Traditionally, tools use threshold-based methods for anomaly detection but struggle to detect complex patterns in modern dynamic networks. This paper investigates leveraging machine learning to augment monitoring capabilities. Key network monitoring tools are described along with how they currently handle anomaly detection. Machine learning techniques for developing predictive models from historical data are then discussed. A framework for integrating trained models as add-ons to existing tools is proposed. These AI-driven approaches are shown to provide more accurate and automated anomaly detection compared to legacy techniques.

Проектирование интеллектуальной противопожарной системы

Fire protection systems utilize detectors that process signals from fire sensors using threshold-based methods and gen-erate a fire signal based on a logical function. Artificial neural networks can enhance these detectors by processing in-formation from a network of sensors after being trained. To train these neural networks, extensive data sets are neces-sary, which can be obtained through fire simulations on a supercomputer. Field tests are costly, subject to random fac-tors, limited to one or two rooms, and do not provide a comprehensive picture of fire development. Thus, designing intelligent fire systems falls under model-based design. Through modeling, large data sets were generated for training fire system algorithms, expanding the range of tasks they can address. A group of neural networks is proposed for op-timizing the placement of multi-parameter sensors, identifying the type of burning material, detecting fires at early stages, and localizing the fire zone to select appropriate extinguishing agents. Artificial neural networks enable the pre-diction of fire development, mapping hazardous factors' distribution to find optimal evacuation routes. An example of model-based design for a ship fire protection system is provided.

Machine learning driven instance segmentation providing new porosity insights into wire arc directed energy deposited Ti-22V-4Al

Non-destructive x-ray methods such as micro-computed tomography (micro-CT) are useful for investigating porosity defects in additively manufactured products. Understanding the porosity knowledge accessible to micro-CT technologies relies on quantifying the spatial and morphological characteristics of porosity instances. So far, machine learning-based semantic segmentation techniques and threshold-based image analysis methods have been employed for this purpose. However, the reliability of these methods is compromised by their limitations in segmentation accuracy and delineating connected pores. This work proposed a porosity investigation strategy involving a deep learning-based instance segmentation process on micro-CT slices followed by a 3D reconstruction process to reliably examine the spatial and morphological data of 3D porosities, cased studied by a wire arc directed energy deposited Ti-22 V-4Al alloy. Systematic data analysis captured new insights of pore defects in terms of pore formation tendency, the relationship between pore size and pore sphericity, pore evolution, and the discovery of pore-free zones. The proposed workflows are transferrable for the porosity investigation of other additively manufactured components. The new insights may also guide future advancements in porosity elimination and manipulation techniques to fabricate mechanically robust complex structured components.

Defining the optimal segmentation method for measuring somatostatin receptor expressing tumor volume on 68 Ga-DOTATATE positron emission tomography/computed tomography to predict prognosis in patients with gastroenteropancreatic neuroendocrine tumors.

We aimed to compare different segmentation methods used to calculate prognostically valuable volumetric parameters, somatostatin receptor expressing tumor volume (SRETV), and total lesion somatostatin receptor expression (TLSRE), measured by 68 Ga-DOTATATE PET/CT and to find the optimal segmentation method to predict prognosis. Images of 34 patients diagnosed with gastroenteropancreatic neuroendocrine tumor (GEPNET) who underwent 68 Ga-DOTATATE PET/CT imaging were reanalyzed. Four different threshold-based methods (fixed relative threshold method, normal liver background threshold method, fixed absolute standardized uptake value (SUV) threshold method, and adaptive threshold method) were used to calculate SRETV and TLSRE values. SRETV of all lesions of a patient was summarized as whole body SRETV (WB-SRETV) and TLSRE of all lesions of a patient was computed as whole body TLSRE (WB-TLSRE). WB-SRETVs calculated with all segmentation methods were statistically significantly associated with progression-free survival except WB-SRETV at which was calculated using adaptive threshold method. The fixed relative threshold methods calculated by using 45% (WB-SRETV 45% ) and 60% (WB-SRETV 60% ) of the SUV value as threshold respectively, were found to have statistically significant highest prognostic value (C-index = 0.704, CI = 0.622-0.786, P = 0.007). Among WB-TLSRE parameters, WB-TLSRE 35% , WB-TLSRE 40% , and WB-TLSRE 50% had the highest prognostic value (C-index = 0.689, CI = 0.604-0.774, P = 0.008). The fixed relative threshold method was found to be the most effective and easily applicable method to measure SRETV on pretreatment 68 Ga-DOTATATE PET/CT to predict prognosis in GEPNET patients. WB-SRETV 45% (cutoff value of 11.8 cm 3 ) and WB-SRETV 60% (cutoff value of 6.3 cm 3 ) were found to be the strongest predictors of prognosis in GEPNET patients.

Network security AIOps for online stream data monitoring

In cybersecurity, live production data for predictive analysis pose a significant challenge due to the inherently secure nature of the domain. Although there are publicly available, synthesized, and artificially generated datasets, authentic scenarios are rarely encountered. For anomaly-based detection, the dynamic definition of thresholds has gained importance and attention in detecting abnormalities and preventing malicious activities. Unlike conventional threshold-based methods, deep learning data modeling provides a more nuanced perspective on network monitoring. This enables security systems to continually refine and adapt to the evolving situation in streaming data online, which is also our goal. Furthermore, our work in this paper contributes significantly to AIOps research, particularly through the deployment of our intelligent module that cooperates within a monitoring system in production. Our work addresses a crucial gap in the security research landscape toward more practical and effective secure strategies.

Using Ensemble Learning for Anomaly Detection in Cyber–Physical Systems

The swift embrace of Industry 4.0 paradigms has led to the growing convergence of Information Technology (IT) networks and Operational Technology (OT) networks. Traditionally isolated on air-gapped and fully trusted networks, OT networks are now becoming more interconnected with IT networks due to the advancement and applications of IoT. This expanded attack surface has led to vulnerabilities in Cyber–Physical Systems (CPSs), resulting in increasingly frequent compromises with substantial economic and life safety repercussions. The existing methods for the anomaly detection of security threats typically use simple threshold-based strategies or apply Machine Learning (ML) algorithms to historical data for the prediction of future anomalies. However, due to the high levels of heterogeneity across different CPS environments, minimizing the opportunities for transfer learning, and the scarcity of real-world data for training, the existing ML-based anomaly detection techniques suffer from a poor predictive performance. This paper introduces a hybrid anomaly detection approach designed to identify threats to CPSs by combining the signature-based anomaly detection typically utilized in IT networks, the threshold-based anomaly detection typically utilized in OT networks, and behavioural-based anomaly detection using Ensemble Learning (EL), which leverages the strengths of multiple ML algorithms against the same dataset to increase the accuracy. Multiple public research datasets were used to validate the proposed approach, with the hybrid methodology employing a divide-and-conquer strategy to offload the detection of certain cyber threats to computationally inexpensive signature-based and threshold-based methods using domain knowledge to minimize the size of the behavioural-based data needed for ML model training, thus achieving a higher accuracy over a reduced timeframe. The experimental results showed accuracy improvements of 4–7% over those of the conventional ML classifiers in performing anomaly detection across multiple datasets, which is particularly important to the operators of CPS environments due to the high financial and life safety costs associated with interruptions to system availability.

Spatial domain transfer: Cross-regional paddy rice mapping with a few samples based on Sentinel-1 and Sentinel-2 data on GEE

The global population is on the rise, leading to an increased demand for food resources. Accurate mapping and monitoring of paddy rice fields have become crucial for effective food management and yield estimation. Several large-scale paddy rice mapping products with medium-to-high resolution have been made by using threshold-based phenological methods. The main shortcoming is that paddy rice varies in different regions due to differences in climate and planting habits, which results in the limitations of fixed phenological indicator thresholds for large-scale paddy rice mapping. Therefore, paddy rice samples from different regions are needed to account for regional differences between paddy rice and improve paddy rice mapping accuracy. To address two key shortcomings in existing sample generation methods (the non-robustness of DTW distance and the limitation of fixed phenological indicator thresholds for generating samples) and the three difficulties of cross-regional sample generation (different planting times, growth cycles, and planting intensities), this study proposed a novel approach for cross-regional paddy rice sample generation based on spatial domain transfer, achieving cross-regional paddy rice mapping. The accuracy results (OA of 95.16–97.54%) in three study regions with different climatic and topographic conditions in China and improved accuracy (OA improvement of 5.89–19.06%) compared with the method of NESEA-Rice10, proved the effectiveness of the proposed method and the potential of the proposed method in large-scale paddy rice mapping. The proposed method can obtain high-accuracy paddy rice mapping results in regions without paddy rice samples and be applied to the mapping of other crops and land cover types.

A Spatial Optimization Model for Delineating Metropolitan Areas

A metropolitan area comprises a collection of cities and counties bound by strong socioeconomic ties. Despite the pivotal role that metropolitan areas play in regional economics, their delineation remains a challenging task for researchers and urban planners. Current threshold-based delineation methods select counties based on their connection strength with prespecified core counties. Such an approach often neglects potential interactions among outlying counties and fails to identify polycentric urban structures. The delineation of a metropolitan area is fundamentally a spatial optimization problem, whose objective is to identify a set of counties with high interconnectivity while also meeting specific constraints, such as area, contiguity, and shape. In this study, we present a novel spatial optimization model designed for metropolitan area delineation. This model aims to maximize intercounty connection strength in terms of both industry and daily life. This approach ensures a more accurate representation of the multicore structure that is commonly seen in developed metropolitan areas. Additionally, our model avoids the possibility of holes in metropolitan area delineation, leading to more coherent and logical metropolitan boundaries. We provide a mixed-integer programming formulation for the proposed model. Its efficacy is demonstrated by delineating the boundaries of the Nanjing and Lhasa metropolitan areas. This study also delves into discussions and policy implications pertinent to both of these metropolitan areas.

The Early Detection of Faults for Lithium-Ion Batteries in Energy Storage Systems Using Independent Component Analysis with Mahalanobis Distance

In recent years, battery fires have become more common owing to the increased use of lithium-ion batteries. Therefore, monitoring technology is required to detect battery anomalies because battery fires cause significant damage to systems. We used Mahalanobis distance (MD) and independent component analysis (ICA) to detect early battery faults in a real-world energy storage system (ESS). The fault types included historical data of battery overvoltage and humidity anomaly alarms generated by the system management program. These are typical preliminary symptoms of thermal runaway, the leading cause of lithium-ion battery fires. The alarms were generated by the system management program based on thresholds. If a fire occurs in an ESS, the humidity inside the ESS will increase very quickly, which means that threshold-based alarm generation methods can be risky. In addition, industrial datasets contain many outliers for various reasons, including measurement and communication errors in sensors. These outliers can lead to biased training results for models. Therefore, we used MD to remove outliers and performed fault detection based on ICA. The proposed method determines confidence limits based on statistics derived from normal samples with outliers removed, resulting in well-defined thresholds compared to existing fault detection methods. Moreover, it demonstrated the ability to detect faults earlier than the point at which alarms were generated by the system management program: 15 min earlier for battery overvoltage and 26 min earlier for humidity anomalies.

Course Attainment and Program Attainment in Education Systems with the Average and Threshold Aspect.

Abstract— This article offers a comprehensive exploration of Outcome-Based Education (OBE) with a specific focus on assessing course attainment and program attainment through average-based and threshold-based methods. Utilizing a sample dataset, the study showcases the practical application of these methods to evaluate student achievements. The findings presented as average and threshold course outcome (CO) attainment calculation with sample data for a course, provide insights into student performance for course outcomes, elucidating the strengths and limitations of each approach. The study extends its analysis to program attainment, emphasizing the crucial link between CO and PO through mapping. Attainment calculation of a course with threshold and average CO attainment values displays program outcome (PO) attainment values calculated using both average and threshold methods, offering nuanced insights into student achievements within an academic program. An essential takeaway is the significance of interpreting attainment values. Establishing correlations between these values and course content equips educators to enhance cognitive, psychomotor, and affective development through targeted course delivery improvements. In conclusion, this study underscores the synergy between course and program attainment, offering informed insights to educators and institutions. It recognizes the strengths and weaknesses of average and threshold methods and emphasizes the importance of selecting the appropriate method based on specific learning objectives and educational contexts. This research informs assessment strategies for optimizing educational experiences in attainment methods. Keywords — Course Outcome (CO); Program Outcome (PO); Average Attainment; Threshold Attainment; Course Attainment; Program Attainment.

Comparison of Multiple Segmentation Methods for Volumetric Delineation of Primary Prostate Cancer with Prostate-Specific Membrane Antigen-Targeted 18F-DCFPyL PET/CT.

This study aimed to assess the accuracy of intraprostatic tumor volume measurements on prostate-specific membrane antigen-targeted 18F-DCFPyL PET/CT made with various segmentation methods. An accurate understanding of tumor volumes versus segmentation techniques is critical for therapy planning, such as radiation dose volume determination and response assessment. Methods: Twenty-five men with clinically localized, high-risk prostate cancer were imaged with 18F-DCFPyL PET/CT before radical prostatectomy. The tumor volumes and tumor-to-prostate ratios (TPRs) of dominant intraprostatic foci of uptake were determined using semiautomatic segmentation (applying SUVmax percentage [SUV%] thresholds of SUV30%-SUV70%), adaptive segmentation (using adaptive segmentation percentage [A%] thresholds of A30%-A70%), and manual contouring. The histopathologic tumor volume (TV-Histo) served as the reference standard. The significance of differences between TV-Histo and PET-based tumor volume were assessed using the paired-sample Wilcoxon signed-rank test. The Spearman correlation coefficient was used to establish the strength of the association between TV-Histo and PET-derived tumor volume. Results: Median TV-Histo was 2.03 cm3 (interquartile ratio [IQR], 1.16-3.36 cm3), and median TPR was 10.16%. The adaptive method with an A40% threshold most closely determined the tumor volume, with a median difference of +0.19 (IQR, -0.71 to +2.01) and a median relative difference of +7.6%. The paired-sample Wilcoxon test showed no significant difference in PET-derived tumor volume and TV-Histo using A40%, A50%, SUV40%, and SUV50% threshold segmentation algorithms (P > 0.05). For both threshold-based segmentation methods, use of higher thresholds (e.g., SUV60% or SUV70% and A50%-A70%) resulted in underestimation of tumor volumes, and use of lower thresholds (e.g., SUV30% or SUV40% and A30%) resulted in overestimation of tumor volumes relative to TV-Histo and TPR. Manual segmentation overestimated the tumor volume, with a median difference of +2.49 (IQR, 0.42-4.11) and a median relative difference of +130%. Conclusion: Segmentation of intraprostatic tumor volume and TPR with an adaptive segmentation approach most closely approximates TV-Histo. This information might be used to guide the primary treatment of men with clinically localized, high-risk prostate cancer.

An angle-based leak detection method using pressure sensors in water distribution networks

Abstract Leak detection has significant implications for the long-term stable operation of water distribution networks (WDNs). This study presented a novel leak detection method by calculating the angular variance between a pressure vector and other vectors in the database, to evaluate the presence of an anomaly in a network. The top priority for this method was to establish a reliable dataset collected from the pressure sensors, which is generated by EPANET 2.2. Numerous node water demand data in normal conditions were generated by the Monte Carlo method, and leak conditions with various leak flows were simulated by creating leak holes in the pipes. Through learning the composite normal and abnormal data in a certain proportion, the angle-based outlier detection model was employed to identify abnormal events. This angle-based method was applied in an actual WDN and the identification performance for anomalies was compared with that of previous detection methods. The results indicated that the novel method proposed in this study could significantly improve the accuracy and efficiency of leak detection compared to the threshold-based and distance-based detection methods.

Evaluation of Elderly Fall Detection Systems using Data Analysis

As the global population continues to age, it is more important to ensure the safety and wellbeing of senior citizens. Falls are a frequent and dangerous concern for elders, frequently resulting in fatalities or very serious injuries. Modern technology, such as the Elderly Fall Detection System, have been created to solve this issue. This technology attempts to detect falls quickly and react to them, offering aid right away and maybe saving lives. These studies identify a fall when the tri-axial accelerometer reading from a wearable device exceeds the predetermined threshold. Less complexity and computational expense compared to other approaches is one of the main benefits of adopting threshold-based methods. Finding the right threshold value, however, to accurately identify all falls without confounding them with certain ADL, has proven to be a challenging issue. Early Intervention and Medical Aid: Being independent and able to live in one's own house are two things that many elderly people enjoy. Support for carers and peace of mind: detection systems not only aid the elderly but also provide their carers peace of mind. Cost-cutting: Falls among the elderly frequently lead to hospital stays, rehab, and higher healthcare expenses. The Weighted Product Model (WPM) is a technique for ranking and evaluating alternatives based on a variety of factors. It is an easy and obvious method that enables decision-makers to weigh the relative weight of many variables and make wise decisions. Smart surveillance system, Smart cane, Smart Carpet, Smart phone/watch and app. Unobtrusiveness, reliability, privacy, Cost. As the global population continues to age, it is more important to ensure the safety and wellbeing of senior citizens. Falls are a frequent and dangerous concern for elders, frequently resulting in fatalities or very serious injuries The Weighted Product Model (WPM) is a technique for ranking and evaluating alternatives based on a variety of factors Unobtrusiveness, reliability, privacy, Cost, smart phone is the highest and smart carpet is the lowest

Improved pool fire-initiated domino effect assessment in atmospheric tank farms using structural response

Domino effects are severe accident scenarios affecting storage tanks and are often initiated by pool fires. Flame engulfment and heat radiation are the two major sources triggering domino effect. Threshold-based and probit-based methods are widely used to assess the possibility and probability of a secondary accident. These methods are also a part of advanced methods devoted to examining synergic or coupling effects. The current work examines (i) how effective the threshold-based methods are and (ii) how accurate the current time to failure (TTF) estimation models are, which are the basis of probit-based methods. The results suggest that threshold-based methods are not pertinent for the quantitative assessment of domino effect and that significant improvement can be made in the existing TTF prediction models using site-specific structural response data. A new set of equations for TTF estimation using data analytics is proposed. Application to 4,080 pool fire scenarios demonstrates that the newly developed model can improve the TTF prediction performance compared to the existing models (around 22% in terms of R2). In addition, a method has been proposed and validated to correlate time with the failure probability for time-dependent domino effect assessment, which is a limitation of probit-based methods.