Static Source Research Articles

Mechanism of rock bursts in a large inclined extra-thick coal seam under the condition of upper slice mining: a case study

With the increase of mining depth and mining intensity of the large inclined extra-thick coal seam (LIETCS), the number of rock bursts increases significantly. In this paper, based on a rock burst during upper roadway driving of the LIETCS, the distribution characteristics and fracture mechanism of microseismic (MS) events are analyzed using data statistics and seismic moment tensor inversion. By means of mechanical analysis and numerical simulation, this paper studies the static load distribution characteristics and dynamic load action effect of the LIETCS under upper slice mining condition. Based on the theory of dynamic and static combined load, the mechanism of rock bursts is proposed. The results show that the coal and rock mass of the LIETCS are mainly destroyed by shearing. The “shear-clamping” stress produced by the joint action of the roof and floor is the static load source. In upper slice mining, the shear stress and energy of the coal pillar behind the slice and the coal body below the coal pillar always remain high. Under the superposition of floor dynamic load, the regional stress environment changes, which induces rock bursts. This study provides a theoretical basis for rock burst prevention under similar conditions.

Computer Modeling and Electrical Calculations of an Autonomous Power Plant with Static Power Sources

Computer Modeling and Electrical Calculations of an Autonomous Power Plant with Static Power Sources

MCDM-Based Routing for IoT-Enabled Smart Water Distribution Network

The work consists of two subapproaches. In the first approach, an analytical model is developed using trapezium fuzzy numbers in decision-making problems for an Internet of Things-based water distribution network. The second phase explains the integration of the previous phase with the MCDM-based location routing protocol (M-LRP). The water distribution network has three components static water source, the utility center (UC) which can be located in the proper position, and the consumer. The objective of this work is to select an optimal route between the UC and the consumer by considering multiple criteria. The simulation result shows that the proposed multicriterion-based decision-making (MCDM)-based routing protocol outperforms both existing MCDM-based and non-MCDM-based routing schemes. The proposed model outperforms the existing models like non-MCDM-based and MCDM-based routing protocols by 51% and 11%, respectively.

Non-contact acoustic method to measure depth-dependent elastic properties of a kiwifruit

The mechanical properties of a kiwifruit are investigated via non-contact acoustic measurements. Transmitted ultrasonic waves are measured using a static laser source and a rotating laser ultrasound detector. The measurements enable observation of a low-frequency surface wave and a higher frequency direct wave; combined with seismology-inspired theoretical modelling, these measurements enable estimation of several elastic parameters for inner and outer fruit flesh layers. Results indicate that the surface and direct wave velocities and the bulk and Young’s moduli all evolve with fruit age/ripeness, while only the direct wave velocity and bulk modulus differ significantly between outer and inner fruit layers.

Complexity Factor of Static Axial Complex Structures in f(R, T) Gravity

This article investigates the physical features of static axial sources that produce complexity within the matter configuration within the perspective of f(R, T) theory, where R is the curvature invariant and T identifies the trace of matter energy tensor. In this case, the contracted Bianchi identities of effective as well as normal matter are used to develop the conservation equations. We split the curvature tensor to compute structure scalars, involving the physical aspects of the source in the influence of modified factors. We explore the evolving source and compute the complexity of the system. Three complexity factors are determined by using structure scalars; after that, the corresponding propagation equations are explored to investigate the intense gravitational consequences. Finally, the outcomes of irregular anisotropic spheroids are presented using the criterion of vanishing complexity. The f(R, T) corrections are shown to be an additional source of complexity for the axial anisotropic configuration.

Catch Me if You Hear Me: Audio-Visual Navigation in Complex Unmapped Environments With Moving Sounds

Audio-visual navigation combines sight and hearing to navigate to a sound-emitting source in an unmapped environment. While recent approaches have demonstrated the benefits of audio input to detect and find the goal, they focus on clean and static sound sources and struggle to generalize to unheard sounds. In this work, we propose the novel dynamic audio-visual navigation benchmark which requires catching a moving sound source in an environment with noisy and distracting sounds, posing a range of new challenges. We introduce a reinforcement learning approach that learns a robust navigation policy for these complex settings. To achieve this, we propose an architecture that fuses audio-visual information in the spatial feature space to learn correlations of geometric information inherent in both local maps and audio signals. We demonstrate that our approach consistently outperforms the current state-of-the-art by a large margin across all tasks of moving sounds, unheard sounds, and noisy environments, on two challenging 3D scanned real-world environments, namely Matterport3D and Replica. The benchmark is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://dav-nav.cs.uni-freiburg.de</uri> .

Sound source directivity considering source movement

When the source moves, frequency modulation (Doppler effect) occurs in the radiated sound, and the directivity of source changes. In addition, the source can be not located in a direction from the direction of arrival of radiated sound. Therefore, the sound pressure directivity may differ depending on whether the source is static or moving. There are two types of wave equations, one that describes sound pressure as a variable and one that describes velocity potential as a variable. When the sound source moves at a constant velocity and the equation is solved assuming that the source strength is constant with respect to the velocity, the sound pressure directivity of the radiated sound changes depending on the description method of the wave equation. The sound pressure was obtained by solving the wave equations where a single monopole source and a dipole source are moving at a constant velocity. From the results, we showed the difference of sound pressure directivity when source is moving from the directivity of static source.

Geometric control of myosin II orientation during axis elongation.

The actomyosin cytoskeleton is a crucial driver of morphogenesis. Yet how the behavior of large-scale cytoskeletal patterns in deforming tissues emerges from the interplay of geometry, genetics, and mechanics remains incompletely understood. Convergent extension in Drosophila melanogaster embryos provides the opportunity to establish a quantitative understanding of the dynamics of anisotropic non-muscle myosin II. Cell-scale analysis of protein localization in fixed embryos suggests that gene expression patterns govern myosin anisotropy via complex rules. However, technical limitations have impeded quantitative and dynamic studies of this process at the whole embryo level, leaving the role of geometry open. Here, we combine in toto live imaging with quantitative analysis of molecular dynamics to characterize the distribution of myosin anisotropy and the corresponding genetic patterning. We found pair rule gene expression continuously deformed, flowing with the tissue frame. In contrast, myosin anisotropy orientation remained approximately static and was only weakly deflected from the stationary dorsal-ventral axis of the embryo. We propose that myosin is recruited by a geometrically defined static source, potentially related to the embryo-scale epithelial tension, and account for transient deflections by cytoskeletal turnover and junction reorientation by flow. With only one parameter, this model quantitatively accounts for the time course of myosin anisotropy orientation in wild-type, twist, and even-skipped embryos, as well as embryos with perturbed egg geometry. Geometric patterning of the cytoskeleton suggests a simple physical strategy to ensure a robust flow and formation of shape.

What really changes when developers intend to improve their source code: a commit-level study of static metric value and static analysis warning changes

Many software metrics are designed to measure aspects that are believed to be related to software quality. Static software metrics, e.g., size, complexity and coupling are used in defect prediction research as well as software quality models to evaluate software quality. Static analysis tools also include boundary values for complexity and size that generate warnings for developers. While this indicates a relationship between quality and software metrics, the extent of it is not well understood. Moreover, recent studies found that complexity metrics may be unreliable indicators for understandability of the source code. To explore this relationship, we leverage the intent of developers about what constitutes a quality improvement in their own code base. We manually classify a randomized sample of 2,533 commits from 54 Java open source projects as quality improving depending on the intent of the developer by inspecting the commit message. We distinguish between perfective and corrective maintenance via predefined guidelines and use this data as ground truth for the fine-tuning of a state-of-the art deep learning model for natural language processing. The benchmark we provide with our ground truth indicates that the deep learning model can be confidently used for commit intent classification. We use the model to increase our data set to 125,482 commits. Based on the resulting data set, we investigate the differences in size and 14 static source code metrics between changes that increase quality, as indicated by the developer, and changes unrelated to quality. In addition, we investigate which files are targets of quality improvements. We find that quality improving commits are smaller than non-quality improving commits. Perfective changes have a positive impact on static source code metrics while corrective changes do tend to add complexity. Furthermore, we find that files which are the target of perfective maintenance already have a lower median complexity than files which are the target of non-pervective changes. Our study results provide empirical evidence for which static source code metrics capture quality improvement from the developers point of view. This has implications for program understanding as well as code smell detection and recommender systems.

A sound source localization method based on improved second correlation time delay estimation

The sound source localization (SSL) system based on the microphone array has important applications in audio and video conference, security monitoring and intelligent cockpit. However, the SSL method based on time difference of arrival is susceptible to ambient noise. Therefore, an improved second correlation delay estimation algorithm is proposed in this paper. The pure source signal is obtained by wavelet denoising, and then the time delay is calculated by the second correlation time delay estimation algorithm with the weighting functions of the smoothed coherence transform and the Roth processor. The position of the sound target is calculated from the time delay. Aiming at the moving target, an extended Kalman filter is introduced to track the moving trajectory of the sound source. The static and moving SSL simulations are conducted and the results of the proposed algorithm are compared with those of the single-weighted quadratic correlation (SQC) algorithm and the high-power quadratic correlation algorithm. The static sound source positioning errors of the proposed algorithm under −10 dB SNR are respectively 3.97 m and 5.86 m smaller than those of the HQC algorithm and the SQC algorithm. The moving SSL trajectory based on the proposed algorithm is still closest to the real track under −10 dB SNR. This indicates that the proposed algorithm has high precision and strong robustness for sound source location in the low signal-to-noise ratio (SNR) environment. In the experiment, the proposed algorithm can accurately calculate the direction of arrival (DOA) of static sound source and stably track DOA of moving sound source. This is consistent with the simulation results, which further verifies the effectiveness and practicability of the algorithm. This novel algorithm with high time delay estimation accuracy is of great significance for SSL in low SNR environment.

Characterization of Moving Sound Sources Direction-of-Arrival Estimation Using Different Deep Learning Architectures

Sound source localization is an important task for several applications and the use of deep learning for this task has recently become a popular research topic. While a number of previous works has focused on static sound sources, in this paper we evaluate the performance of a deep learning classification system for localization of moving sound sources. In particular, we evaluate the effect of key parameters at the levels of feature extraction (e.g., STFT parameters) and model training (e.g., neural network architectures). We evaluate the performance of different settings in terms of precision and F-score, in a multi-class multi-label classification framework. In our previous work for localization of moving sound sources, we investigated feedforward neural networks under different acoustic conditions and STFT parameters, and showed that the presence of some reverberation in the training dataset can help in achieving better detection for the direction of arrival of the sources. In this paper, we extend the work to show that (1) window size does not affect the performance of static sources but highly affects the performance of moving sources, (2) sequence length has a significant effect on the performance of recurrent architectures, and (3) a temporal convolutional neural network can outperform both recurrent and feedforward networks for moving sound sources.

Pragmatic Evidence on Android Malware Analysis Techniques: A Systematic Literature Review

Alarge number of studies including research articles and surveys on android malware detection and analysis techniques have been presented during the last one and a half decades. The authors proposed different systems and frameworks to identify malware from software applications. However, there is no recent and comprehensive systematic literature review on the detection and analysis of android malware methods, systems, and frameworks. We present a systematic review of literature on android malware detection and analysis techniques and tools by following standard guidelines for Systematic Literature Review methodology from 2010 to 2021. We selected 75 most relevant studies out of 3343 published studies. We found that the prominent malicious datasets are Genome (39%) and Drebin (36%) used by different researchers for the detection of malware. The static, dynamic, and hybrid source code analysis methods are applied by android malware detection techniques. We also identified the limitations and future research directions of existing techniques as research gaps for the community. Based on the pragmatic evidence of this research, we have proposed a hybrid analysis-based multiple feature analysis framework. This framework will not only address the limitations of static and dynamic-based approaches, but it also analyzes evolving android malware datasets using deep neural network and machine learning techniques and improve the accuracy of evolving malware samples.

Computer modeling and electrical calculations of an autonomous power plant with static power sources

Computer modeling and electrical calculations of an autonomous power plant with static power sources

A Bayesian model for human directional localization of broadband static sound sources

Humans estimate sound-source directions by combining prior beliefs with sensory evidence. Prior beliefs represent statistical knowledge about the environment, and the sensory evidence consists of auditory features such as interaural disparities and monaural spectral shapes. Models of directional sound localization often impose constraints on the contribution of these features to either the horizontal or vertical dimension. Instead, we propose a Bayesian model that flexibly incorporates each feature according to its spatial precision and integrates prior beliefs in the inference process. The model estimates the direction of a single, broadband, stationary sound source presented to a static human listener in an anechoic environment. We simplified interaural features to be broadband and compared two model variants, each considering a different type of monaural spectral features: magnitude profiles and gradient profiles. Both model variants were fitted to the baseline performance of five listeners and evaluated on the effects of localizing with non-individual head-related transfer functions (HRTFs) and sounds with rippled spectrum. We found that the variant equipped with spectral gradient profiles outperformed other localization models. The proposed model appears particularly useful for the evaluation of HRTFs and may serve as a basis for future extensions towards modeling dynamic listening conditions.

Performance analysis of extracting object structure from source code

Tools for programmers are constantly evolving, and with that evolution comes tools that support their work, such as those that enable testing and analysis of program code. It is expected that tools integrated with programming environments will analyze code on the fly, and the results of the analysis will be available as soon as the program is compiled. This paper attempts to compare solutions for extracting object-oriented structure that begin the process of static source code analysis. Choosing the right solutions already at the beginning of static analysis, will affect the speed of the whole process as well as accuracy. And this will then affect the cost of programmers' work and the quality of the software tests performed.

КОМБІНОВАНІ ПІДХОДИ ДО СТАТИЧНОГО АНАЛІЗУ КОДУ З ВИКОРИСТАННЯМ НЕЙРОННИХ МЕРЕЖ

This article presents an overview of possible approaches to the application of neural networks in the process of static code analysis. It explores the current state of affairs in existing approaches to improving program analysis using machine learning methods, including postprocessing of static analysis alerts, preprocessing of source code, or direct use of machine learning for analyzing source code. Additionally, the article examines the main directions for applying approaches from each category. Both classical approaches and machine learning methods in program analysis possess distinct strengths and weaknesses that should be considered when implementing them in practice. One of the main theses of this research is that understanding the capabilities of combining these approaches, leveraging the flexibility offered by neural networks while maintaining a sufficient level of reliability provided by classical algorithms, is crucial for building a high-quality system. This article covers the following three basic directions of the application of neural networks for the static source code analysis. The first direction is a specification tuning: a refinement of specifications produced by a ‘classic’ static code analyzer (a removal, clustering, ranking of warnings or just assistance in manual warning analysis, etc.). The second direction is a specification inference, to find specifications hidden in code (feature extraction, selection, or code transformation retaining its behaviour, e.g. to make it more suitable for the ‘classic’ static analysis tools). The third way is a black box analysis to discover and fix code defects (syntactic, semantic ones or vulnerabilities), to assist in manual code checking, to format the code automatically or to find code smells (in this direction only a machine learning model is used, its training is performed on the source code directly). The article outlines directions for the future research which will focus on the development and combining of the approaches covered here.

Adaptive Domain Generalization Via Online Disagreement Minimization.

Deep neural networks suffer from significant performance deterioration when there exists distribution shift between deployment and training. Domain Generalization (DG) aims to safely transfer a model to unseen target domains by only relying on a set of source domains. Although various DG approaches have been proposed, a recent study named DomainBed (Gulrajani and Lopez-Paz, 2020), reveals that most of them do not beat simple empirical risk minimization (ERM). To this end, we propose a general framework that is orthogonal to existing DG algorithms and could improve their performance consistently. Unlike previous DG works that stake on a static source model to be hopefully a universal one, our proposed AdaODM adaptively modifies the source model at test time for different target domains. Specifically, we create multiple domain-specific classifiers upon a shared domain-generic feature extractor. The feature extractor and classifiers are trained in an adversarial way, where the feature extractor embeds the input samples into a domain-invariant space, and the multiple classifiers capture the distinct decision boundaries that each of them relates to a specific source domain. During testing, distribution differences between target and source domains could be effectively measured by leveraging prediction disagreement among source classifiers. By fine-tuning source models to minimize the disagreement at test time, target-domain features are well aligned to the invariant feature space. We verify AdaODM on two popular DG methods, namely ERM and CORAL, and four DG benchmarks, namely VLCS, PACS, OfficeHome, and TerraIncognita. The results show AdaODM stably improves the generalization capacity on unseen domains and achieves state-of-the-art performance.

MAIN DIRECTIONS FOR THE DEVELOPMENT OF SHIP ELECTROTECHNICS AND AUTOMATION

In recent decades, the level of electrification and automation of ships has grown significantly. This is due to the increased requirements for technical, operational and technical and economic characteristics on the part of shipowners. The implementation of the requirements has become possible due to the development of related branches of science, engineering and technology: materials science, power converter technology, microprocessor electronics, and information technology. The main directions of development of ship electrical engineering and automation, namely, improvement of power generation and distribution systems, improvement of electric propulsion systems and propulsion systems, improvement of automation systems are considered in the paper. The expediency of using valve generator sets and the transition to electric power distribution on direct current is shown. Static power sources of new generation are actively developing and spreading both in industry and in maritime transport. Good control characteristics, no restrictions on the number of reverses and minimum speed, high efficiency when operating at fractional loads, high torque overload capacity on the propeller significantly expand the scope of modern AC electric propulsion systems. The desire to combine the advantages of different types of propulsion systems has stimulated the creation of combined (hybrid) propulsion systems. Combined installations incorporate a traditional thermal main engine and a propulsion electric motor. The activation of one or another engine is carried out depending on the mode of operation and the speed of the vessel. The introduction of information technologies makes it possible to automate the management and control of maintenance and repair of ship equipment. The modern development of software and hardware and algorithmic support makes it possible to create unmanned ships that can be controlled remotely and move independently (autonomously) according to predetermined algorithms, including algorithms for divergence from other ships at sea. It is concluded that the main tasks within the framework of the directions for development of ship electrical engineering and automation are increasing the energy efficiency of the process for generating electricity by introducing valve generator sets, introduction of innovative sources of electricity with high rates of efficiency and environmental friendliness, including static sources of electricity, the use of rowing electric motors with permanent magnets and inductor type, the use of combined propulsion systems with reversible electric propulsion systems, and increasing the level of automation through the introduction of information technology and artificial intelligence.

Characteristics of False-Positive Active Fires for Biomass Burning Monitoring in Indonesia from VIIRS Data and Local Geo-Features

In this study, we explored the characteristics of thermal anomalies other than biomass burning to establish a zone map of false-positive active fires to support efficient ground validation for firefighters. We used the ASCII file of VIIRS active fire data (VNP14IMGML), which provides attributes of thermal anomalies every month from 2012 to 2020 in Indonesia. The characteristics of thermal anomalies other than biomass burning were explored using fire radiative power (FRP) values, confidence levels of active fire, fire pixel areas, and their allocations to permanent geographical features (i.e., volcano, river, lake, coastal line, road, and industrial/settlement areas). The Tukey test showed that there was a significant difference between the mean FRP values of the other thermal anomalies, type-1 (active volcano), type-2 (other static land sources), and type-3 (detection over water/offshore), at a confidence level of 95%. Most thermal anomalies other than biomass burning were in the nominal confidence level with a fire pixel area of 0.21 km2. High spatial images validated these thermal anomaly types as false positives of biomass burning. A zone map of potential false-positive active fire for biomass burning was established in this study by referring to the allocation of thermal anomalies from permanent geographical features. Implementing the zone map removed approximately 13% of the VIIRS active fires as the false positive of biomass burning. Insights gleaned through this study will support efficient ground validation of actual forest/land fires.

Flight Demonstration of Net Electric Charge Control of Aircraft Using Corona Discharge

This article reports on a flight experiment demonstrating the ability to control the net electrical charge of a small unmanned aircraft using ion emission from a corona discharge. The charging mechanism is based on the advection of ions produced by the corona discharge, leaving charge of the opposite polarity on the aircraft. An onboard high voltage power supply of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 15 kV was remotely operated to control the level of charging. The high voltage terminal of the power supply was connected to two thin corona wires placed above the aft surface of the wing in the spanwise direction, whereas the low voltage terminal was connected to the conductive airframe. Using this strategy, electric potentials of up to −30 and +23 kV relative to the environment were acquired by the aircraft, using positive and negative corona discharges, respectively. The study addresses the dependencies with power supply bias and wind speed. Possible applications of net charge control include risk reduction of aircraft-triggered lightning strikes, as explored by the authors in prior work, as well as a means of compensating for precipitation static and other sources of charging.