Summary
Incremental dynamic analysis (IDA) leads to curves expressed in terms of structural response versus intensity, commonly known as the IDA curves. It is known that implementation of IDA usually ...involves significant computational effort and most often significant scaling of the original records to various intensity levels. Employing as the performance variable the critical demand to capacity ratio (DCR) throughout the structure, which is equal to unity at the onset of the limit state, facilitates the identification of the intensity values at the onset of a desired limit state and hence the implementation of the IDA procedure. Employing the structural response to un‐scaled records and the corresponding regression‐based response predictions (a.k.a., the “Cloud Analysis”) helps in identifying the range of intensity values corresponding to demand to capacity ratio values in the vicinity of unity. The Cloud to IDA procedure for structural fragility assessment is proposed on the premise of exploiting the Cloud Analysis results to obtain the IDA curves both with minimum number of analyses and minimum amount of scaling. The transverse frame of a shear‐critical 7‐story older RC building in Van Nuys, CA, which is modeled in Opensees with fiber‐section considering the flexural‐shear‐axial interactions and the bar slip, is employed as a case study. It is demonstrated, by comparing the results to IDA and other state of the art non‐linear dynamic procedures based on no scaling or spectral‐shape‐compatible scaling, that the Cloud to IDA procedure leads to reliable results in terms of structural fragility and risk for the prescribed limit state.
Debugging is an important task to identify the defects in the software. Especially, logging is an important feature of a software system to record runtime information. Detailed logging allows ...developers to collect run-time information when they cannot use an interactive debugger, such as continuous integration and web application server cases. However, extensive logging leads to larger execution traces because few instructions can be repeated many times. In our previous work, to record detailed program behavior within limited storage space constraints, we proposed near-omniscient debugging, which is a methodology that records and visualizes an execution trace using fixed size buffers for each observed instruction. In this paper, we evaluate the effectiveness of near-omniscient debugging in recording infected states while reducing the size of execution traces. We conduct experiments on the Defects4J dataset and evaluate the effectiveness based on the completeness, trace size and runtime overhead. The result shows that near-omniscient debugging can completely record infected states for nearly 80 percent of bugs (with a buffer size of 1024 events). The size of execution traces can be reduced by a factor of one thousand for large repetitive executions.
•Effectiveness evaluation of near-omniscient debugging with 831 actual bugs.•Keeping the majority of infected states.•Predictable trace size from the number of methods.•Reducing execution time significantly when all tests are executed.
Structural Health Monitoring (SHM) is critical in ensuring the safety and longevity of almost all engineering infrastructure we rely on today. Conventional methods are often costly, requiring ...specialised equipment and expertise. With continual reductions in computational processing times and component costs, photogrammetry – the process of extracting information from photographs – could offer a promisingly affordable alternative for such analysis. This paper delves into the utilisation of the most well-established techniques in this burgeoning field of research and proposes a novel alternative: Mean Intensity Mapping (MIM). These were experimentally tested and compared to existing (often more expensive) methods of acquiring data for dynamic analysis, such as laser vibrometers and accelerometers, to gauge accuracy, viability, and ease of use. Experimental testing included preliminary studies with varying mass, structural degradation, and tracking frame complexity; followed by in-situ testing at the Clifton Suspension Bridge (CSB). The aim was to validate a low-cost method of extracting modal data from video recordings, such that these methods could be applied to Structural Health Monitoring (SHM). An entry-level Panasonic camcorder (£200) was used for its high optical zoom; which allowed for frequencies to be detected from a distance using both the conventional tracking algorithms and the novel method (employing brightness-based virtual sensors). These frequencies were verified by comparing them to models and past studies on the CSB. Natural frequencies recorded from the photogrammetric methods under favourable conditions were notably very similar in accuracy to conventional methods (with ∼0.5% error), and they therefore have many practical applications. This demonstrates that photogrammetry can be performed at a lower-cost to conventional methods, allowing surveyors and engineers to make observations and detections in the space of a few minutes, in a non-contact way, and from a distance. There are still a number of barriers which must be overcome including variations in visibility due to weather, changes in the refractive index of air as a result of wind/temperature, difficulty gathering data at night time or in low light, processing/calculation times, and tripod/mounting instability. However, it is very conceivable that as the cost of optical equipment and processing is decreasing, photogrammetric methods are likely to become an indispensable mechanism in SHM of the future.
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•Mean Intensity Mapping (MIM) is a simple new method of real-time signal processing.•Varied mass and in-situ testing shows MIM’s efficacy in extracting modal data.•In good lighting low-cost equipment found frequencies below the Nyquist limit within 0.5%.•Good external lighting was one of the biggest challenges for frequency detection.•Gaussian smoothing levels reduced higher frequency detection capability.
A ransomware is a unique class of malware which has gotten extremely famous in digital crooks to corkscrew cash. It categorizes the client confines by accessing their machines (PCs, cell phones and ...IoT gadgets) unless the payoff is paid. Consistently, security specialists report numerous types of ransomware assaults, including ransomware families. User's data will be collected at the time of dynamic process. The collected data will be in crypto ransomware type from that we can extract features like IP address, file length, URL. We will do dynamic analyse of the presently data with the antecedent data. Using machine learning algorithm (by combining Random Forest, Gradient Tree Boosting and Support Vector machine algorithm) we can classify the data as benign or ransomware. The achievement rate of classification using machine learning algorithm is 98.45% with false rate 0.01.The proposed achievement rate will be compared among linear regression, navie Bayes and adaboost algorithm. Gandcrab ransomware-Version, algorithm is to be identified.
The method of incremental dynamic analysis (IDA) is one of the most accurate nonlinear seismic analyses. IDA is a time-consuming method and has a high computational cost. Also, an IDA curve should be ...obtained from the building analysis subjected to a series of earthquakes that should cover all regions of the structural responses. In this paper, the discrete wavelet transform is used to solve these problems. The results showed that the use of the filtered records reduces the computational cost and time about 87.4% and 73%, respectively, while the maximum error is just about 7.6%.
•2D and 3D nonlinear pushover and incremental dynamic analyses (IDA) were performed.•Using OpenSees the behavior of a 4-story modular steel building (MSB) was evaluated.•Diaphragm interactions, ...relative displacements and rotations of modules were studied.•The force transfer through connections and column discontinuities are considered.
Modular steel construction is a relatively new construction technique that considerably reduces the time spent on the construction site. However, due to the detailing and assembly requirements of multi-story modular steel buildings (MSBs), these systems are prone to undesirable failure mechanisms during large earthquakes. In this paper a 4-story MSB is designed considering realistic constraints posed during the modular construction. Using a detailed model in OpenSees an assessment of the seismic demand and capacity of this MSB is provided by performing nonlinear static pushover and incremental dynamic analyses (IDA) in two and three dimensions. Diaphragm interactions, relative displacements and rotations between modules, the force transfer through horizontal connections, column discontinuity coupled with possible high inelasticity concentration in vertical connections are some other important aspects that are specifically considered. The results that are summarized with relevant conclusions provide a better insight to the dynamic behavior of multi-story MSBs.
In this paper, two-stage approaches are proposed to study damage detection, localization and quantification in Functionally Graded Material (FGM) plate structures. Metal and Ceramic FGM plates are ...considered using three different composite materials: Al/Al2O3, Al/ZrO2-1, and Al/ZrO2-2. The FGM plates are modelled using IsoGeometric Analysis (IGA), which is more efficient than the classical Finite Element Method (FEM). Using a power-law distribution of the volume fractions of the plate constituents, the material properties of the plates are expected to vary continuously through their thickness. Improved damage indicator based on Frequency Response Function (FRF) is employed to predict the damaged elements in the first stage. A robust and efficient Improved Artificial Neural Network using Arithmetic Optimization Algorithm (IANN-AOA) is implemented for damage quantification problem in the second stage. The main idea is based on eliminating the healthy elements from the numerical model by the improved indicator. Next, collected data from damaged element based on damage index of an improved indicator is used as input and damage level as output. To prove the robustness of IANN-AOA, a Balancing Composite Motion Optimization (BCMO) is considered to improve ANN (IANN-BCMO) and is used for comparison. The results show that the improved indicator can predict the damaged elements with high precision. For damage quantification, IANN-AOA provides more accurate results than IANN-BCMO.
Summary
It is desirable that nonlinear dynamic analyses for structural fragility assessment are performed using unscaled ground motions. The widespread use of a simple dynamic analysis procedure ...known as Cloud Analysis, which uses unscaled records and linear regression, has been impeded by its alleged inaccuracies. This paper investigates fragility assessment based on Cloud Analysis by adopting, as the performance variable, a scalar demand to capacity ratio that is equal to unity at the onset of limit state. It is shown that the Cloud Analysis, performed based on a careful choice of records, leads to reasonable and efficient fragility estimates. There are 2 main rules to keep in mind for record selection: to make sure that a good portion of the records leads to a demand to capacity ratio greater than unity and that the dispersion in records' seismic intensity is considerable. An inevitable consequence of implementing these rules is that one often needs to deal with the so‐called collapse cases. To formally consider the collapse cases, a 5‐parameter fragility model is proposed that mixes the simple regression in the logarithmic scale with logistic regression. The joint distribution of fragility parameters can be obtained by adopting a Markov Chain Monte Carlo simulation scheme leading directly to the fragility and its confidence intervals. The resulting fragility curves compare reasonably with those obtained from the Incremental Dynamic Analysis and Multiple Stripe Analysis with (variable) conditional spectrum–compatible suites of records at different intensity levels for 3 older reinforced concrete frames with shear‐, shear‐flexure‐, and flexure‐dominant behavior.
Android based smartphones have become a top target for malware writers due to their widespread use. A number of malicious applications are present on play stores and downloaded on daily basis, posing ...a significant threat to users’ personal and business data. As a result, the design of malware analysis frameworks is crucial in protecting the growing number of users who rely on their smart phones for routine and business tasks. The traditional signature based schemes for malware detection are unable to handle new and sophisticated malware. Furthermore, generic solutions based on static analysis schemes become less effective in the presence of obfuscated malware. In this study, a dynamic analysis based framework, VolMemDroid, for detecting malicious applications for Android is proposed. The framework extracts the volatile memory artifacts for profiling malicious Android applications. For this purpose, the memory forensic framework of volatility is utilized. A number of volatility plugins are analyzed for their compatibility w.r.t the Android platform and their ability in modeling the application’s behavior. After testing a number of plugins, chosen plugins are further processed for extraction of features. A comprehensive feature set for Android malware detection and categorization is proposed. It has been found that the suggested framework is effective for detecting Android malicious applications with an F1-score of 0.972, which is better than existing volatile memory based approaches for Android malware detection. The framework is also found to be effective in categorizing malicious Android applications into four distinct classes.
•A novel data driven malware detection and categorization framework for Android.•Resilient against obfuscation by utilizing volatile memory-based features.•First study to profile Android apps using memory forensics framework of volatility.•Volatility Plugins are analyzed for generation of useful feature representations.•Comprehensive feature set is reported for effective Android application analysis.
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