Identification Of Paths Research Articles

Spatio-temporal variability of surface temperatures in High Arctic periglacial environment using UAV thermal imagery and in-situ measurements

ABSTRACT The rapidly changing climate in the High Arctic is posing significant challenges to its ecosystem. To better understand the changes, this study uses Uncrewed Aerial Vehicle (UAV) technology equipped with a thermal camera to investigate ground and water temperatures in the catchments of southwest Spitsbergen. This approach provides high-resolution, real-time data, offering a detailed understanding of surface temperatures in this remote and harsh environment. Integrating UAV technology with in-situ measurements enables data collection at high spatial and temporal scales, facilitating a thorough analysis of temperature trends and patterns. The research introduces a new processing workflow using open-source packages to create thermal orthomosaics, enhancing the analysis’s accuracy and efficiency. The final developed orthomosaics examine spatial variations in Land Surface Temperature (LST) and Water Surface Temperature (WST) and explore factors influencing them. Additionally, the study investigates thermal patterns in Arctic hydrological systems, such as channels and ponds, allowing for the identification and characterization of flow paths, including groundwater intrusions and surface water mixing, within the catchments. This multidimensional approach aims to provide valuable insights into the complex interactions between cryo-, hydro-, and meteorological dynamics in the High Arctic.

Identification of Distinct Visual Scan Paths for Pathologists in Rare-Element Search Tasks.

The search for rare elements, like mitotic figures, is crucial in pathology. Combining digital pathology with eye-tracking technology allows for the detailed study of how pathologists complete these important tasks. To determine if pathologists have distinct search characteristics in domain- and nondomain-specific tasks. Six pathologists and six graduate students were recruited as observers. Each observer was given five digital "Where's Waldo?" puzzles and asked to search for the Waldo character as a nondomain-specific task. Each pathologist was then given five images of a breast digital pathology slide to search for a single mitotic figure as a domain-specific task. The observers' eye gaze data were collected. Pathologists' median fixation duration was 244 ms, compared to 300 ms for nonpathologists searching for Waldo (P < .001), and compared to 233 ms for pathologists searching for mitotic figures (P = .003). Pathologists' median fixation and saccade rates were 3.17/second and 2.77/second, respectively, compared to 2.61/second and 2.47/second for nonpathologists searching for Waldo (P < .001), and compared to 3.34/second and 3.09/second for pathologists searching for mitotic figures (P = .222 and P = .187, respectively). There was no significant difference between the two cohorts in their accuracy in identifying the target of their search. When searching for rare elements during a nondomain-specific search task, pathologists' search characteristics were fundamentally different compared to nonpathologists, indicating pathologists can rapidly classify the objects of their fixations without compromising accuracy. Further, pathologists' search characteristics were fundamentally different between a domain-specific and nondomain-specific rare-element search task.

Automated Weld Path Generation in Cluttered Environments Using Segmentation and Iterative Closest Point Workpiece Localization

Abstract A significant amount of manufacturing is performed by small to medium enterprises (SMEs), but these manufacturers often have lower adoption rates of automation. The cost and complexity associated with traditional robot systems slow the adoption of robotic welding operations for SMEs. The recent increase in collaborative robotic welding systems is bridging the gap, however, by reducing the complexity of installing, maintaining, and training operators to perform weld operations with collaborative robotics. These systems add flexibility in the range of operator use and ease of deployment. These systems however still rely on kinematic registration between the robot-mounted torch and workpiece on any open-loop weld. This requires precise placement of the workpiece prior to performing a weld. This work will discuss a method for part identification and registration in a welding task as a step toward automated weld path generation. The method is based on lower-resolution 3D cameras (RGBD cameras) using a combination of color and depth information. This information is used to both identify the workpiece within a workspace that may have other, non-workpiece items, and then provide registration or localization information of the workpiece within a resolution that could allow follow-on near-position strategies to achieve final weld path identification.

Identification of key paths for embodied carbon emissions in China based on a five-dimensional analysis model

China proposed the "double carbon goals" in 2020. carbon emissions reduction has become a crucial aspect in China's economic development process. Faced with significant differences in regional carbon emissions, and complex inter-industry linkages, it is necessary to break down the carbon emission paths along the supply chain from multiple dimensions. Based on the input-output-structure path analysis model, we constructed a five-dimensional analysis model, aiming to thoroughly clarify China's inter-regional and inter-industry carbon emission relations. The results show that from 1997 to 2017, carbon emissions driven by urban household consumption have increased from 579.48 Mt to 3302.71 Mt. Transportation warehousing and other services have driven upstream to generate lots of carbon emissions. The largest emission path is generated by the electric heat and water supply industry in Shandong, meeting the consumption of urban residents. Carbon emissions generated by electric heat and water supply industry in Shanxi and Shandong should be focused.

HT-PGFV: Security-Aware Hardware Trojan Security Property Generation and Formal Security Verification Scheme

Property-driven hardware verification provides a promising way to uncover design vulnerabilities. However, developing security properties that check for highly concealed security vulnerabilities remains a significant challenge. In this paper, we propose a scheme, called HT-PGFV, to implement hardware Trojan security property assertion automatic generation and formal security verification for Trojan-infected designs. In our scheme, we develop a hardware Trojan security property assertion generation method for automated hardware which can extract hardware Trojan security properties from Trojan-infected designs by performing the three main steps of Trojan-infected signal identification based on feature matching, influence-cone-analysis-based Trojan path identification, and information flow trace mining, and formulate them as SystemVerilog assertions. In addition, we develop a formal security verification method based on information flow analysis which can formally verify hardware Trojan security properties and detect hardware Trojans violating information flow security policies by checking the security of information flows via our developed RT-level hardware information flow security models. The proposed method is demonstrated on several Trojan benchmarks from Trust-Hub. Experimental results show that our scheme can generate hardware Trojan security property assertions for Trojan-infected designs and detect information leakage and functionality change hardware Trojans activated by external inputs or internal conditions.

Geometric deep learning framework for de novo genome assembly.

The critical stage of every de novo genome assembler is identifying paths in assembly graphs that correspond to the reconstructed genomic sequences. The existing algorithmic methods struggle with this, primarily due to repetitive regions causing complex graph tangles, leading to fragmented assemblies. Here, we introduce GNNome, a framework for path identification based on geometric deep learning that enables training models on assembly graphs without relying on existing assembly strategies. By leveraging only the symmetries inherent to the problem, GNNome reconstructs assemblies from PacBio HiFi reads with contiguity and quality comparable to those of the state-of-the-art tools across several species. With every new genome assembled telomere-to-telomere, the amount of reliable training data at our disposal increases. Combining the straightforward generation of abundant simulated data for diverse genomic structures with the AI approach makes the proposed framework a plausible cornerstone for future work on reconstructing complex genomes with different ploidy and aneuploidy degrees. To facilitate such developments, we make the framework and the best-performing model publicly available, provided as a tool that can directly be used to assemble new haploid genomes.

Coupling adaptive filters with acoustic travel time tomography for indoor climate measurements in occupied room conditions

Acoustic travel time TOMography (ATOM) as a remote sensing technique allows for the measurement of indoor climate parameters, such as air temperature and airflow velocity distributions. The performance of ATOM in monitoring indoor air temperature in empty room conditions was evaluated recently, utilizing a combined analysis approach of room acoustics and tomography. This study shifts focus to the assessment of ATOM in an occupied room condition where fixed objects are introduced to the test area. The presence of objects in a room can result in notable shifts in the travel times of individual reflections. In some cases, these shifts can cause reflections to disappear completely or be replaced by new ones. These partial disruptions in the reflection pattern present challenges in obtaining accurate information about the room's temperature. To ensure the accurate identification of undisturbed reflection paths, an algorithm was developed that can convert distorted signals into a pure reference signal using a suitable adaptive filter. The measurement results confirm the validity of the approach for a temperature interval of 4°C where the system was trained for two temperatures of 20°C and 24°C in a climate chamber.

Hybrid optimization-based Effective Path Selection and Multipath routing in MANETs

Mobile Adhoc Networks (MANETs) have emerged as a popular technology because of their wide application and independent mobility. Nevertheless, communication through MANETs can be effective because of their inherent nature of high mobility, which in turn results in a higher rate of data loss and delay. Therefore, for effective communication to be achieved, the MANETs’ routing performance must be enhanced. Some extant routing protocols like AOMDV can yield moderate Routing Overhead (RO) during communication. The review of previous studies shows that there is a need for improvement in the following areas: Computational E2E delay: the proposed Genetic Algorithm- Ant Colony Optimization (GA-ACO) was unable to reduce the computational E2E delay that occurs during communication. RO: The schemes that were proposed failed to minimize the RO in the network. Energy consumption: the reduction energy consumed by the network was reduced, however, high throughput and PDR could not be achieved by the method. On the contrary, the EHO-based AOMDV routing protocol achieved low E2E delay and RO but was unable to decrease the energy consumed by the network. Throughput and PDR: The protocol proposed failed to achieve high Quality of Services (QoS). Consequently, this paper aims to provide a solution to this communication problem by proposing a Hybrid Optimization-based Effective Path Selection and Multipath routing (HOEPSM) approach for MANETs. The proposed approach, which is HOEPSM is made up of two parts: Elephant Herding Optimization (EHO)-based AOMDV routing protocol, which makes use of EHO for the identification of the shortest path from the source to the destination. The second part which is the Hybrid optimization using Genetic Ant Optimization (GAO) algorithm, involves finding the best solution through the use of a GAO-based hybrid optimization algorithm. The results of the study revealed that the use of the HOEPSM causes a significant decrease in routing RO while enhancing the network’s Quality of Forwarding (QoF) of the network. Also, the simulation of the proposed approach was carried out in NS2, and afterward, compared with earlier proposed methods like DRARP and MDRARP. Based on the simulation results, the HOEPSM outperformed the other methods, with high Energy Efficiency (EE) (91%), Packet Delivery Ratio (PDR) (95%), RO (155), and End to End (E2E delay) (102ms). These results can be attributed to the HOEPSM’s choice of the best and steady route from the source nodes and destination nodes.

Retraction Note: Super-imposed cluster embedding for ring routing path identification in WSN

Retraction Note: Super-imposed cluster embedding for ring routing path identification in WSN

An accurate identification and spatial characterization method for the development degree of preferential flow paths in water-flooded reservoir

Water flooding is one of the most important methods for oil field development, It accounts for more then 70% of China's crude oil production. However, in the progress of water flooding, preferential flow paths often formed between oil and water wells, which seriously restricts production rate. How to effectively identify the preferential flow paths has become the key to improving the effect of water flooding. In this paper, to solve the problem of difficult identification of preferential flow paths in reservoirs, through the change of core seepage law with high pore volume water flooding experiment, parameters such as multiple of water flux, change value of permeability, and water saturation were selected for analysis. The weight coefficient for each parameter was determined by the variation coefficient method of objective weight. Subsequently, a comprehensive reservoir identification index was obtained by weighting each parameter, which was used to describe the development degree of the preferential flow paths. Finally, quantitative criteria of preferential flow paths were given. The spatial characterization of preferential flow paths was realized by post processing of the Eclipse software. The new method for identifying preferential flow paths fully considers the changes in physical properties and fluid mobility of water-flooded reservoirs. The results of the new applied to a typical water-flooded reservoir in the Bohai Bay Basin show that the preferential flow paths calculated by the new method were highly consistent with the judgment results of tracers. It can accurately and quickly identify the preferential flow paths. This study provides a scientific basis for adjusting measures of water-flooded reservoirs to further enhance oil recovery. Moreover, the new method holds broader prospects for application in the field of porous media transport.

Identification of Intra and Inter Subsystems Failure Propagation Path for Incorporation of Preventive Measures in A Spacecraft - A Case Study

All failure modes down to individual part level needs to be addressed for a space system as there is a great deal of concern not only with the probability of failure of parts but also with the potential consequences of different modes of failure. It is more so in space related systems as they are non repairable after launch. Towards this, Failure mode effects analysis needs to be carried out at system level for a spacecraft to identify intra and inter subsystems failure propagation paths to incorporate preventive measures in the design. A case study on interplanetary mission is provided in this paper to explain the significance and necessity of system level failure mode effects analysis. This paper critically addressed the effects of a failure on built-in autonomy and fault tolerant design of interplanetary mission considered for the case study.

Hybrid fuzzy logic and gravitational search algorithm based routing for wireless sensor networks

Over the recent years, the wireless sensor network (WSN) has garnered significant attention from both researchers and the general populace. Its application in diverse environmental scenarios, including weather monitoring, temperature regulation, humidity tracking, and military surveillance, extends beyond conventional boundaries. WSNs consist of numerous nodes, each functioning as a sensor with the primary responsibility of data sensing. These nodes operate under constraints such as power, energy, efficiency, and deployment considerations. Moreover, the power and other resources cannot be replaced and renewed therefore prolonging the network lifetime has become the main aspect of WSNs. Energy aware routing play’s important role to ensure the efficient data transmission with minimal power consumption and ensures the prolonged network lifetime. In this work, we focus on optimizing the routing process therefore we present a hybrid model which uses fuzzy logic for path identification and gravitational search optimization (GSA) for efficient path selection. The fuzzy logic considers energy consumption, residual energy, distance and delay parameters to identify the most suitable path for data transmission. The experimental analysis shows a significant improvement in network lifetime, delay an aspect delivery.

Dynamic fracture mechanism from a pre-existing flaw in granite: Insights from grain-based modelling

Both heterogeneity and pre-existing flaws are closely associated with the mechanical response of crystalline granite. To uncover the underlying mechanism, grain-based model (GBM) based on particle flow code (PFC), i.e., PFC-GBM, with different contacts between any two grains, was employed to carry out numerical calculations to investigate the effect of a single flaw with various inclination angles on the dynamic behavior of LdB granite. The failure pattern is identified through density and spatial distributions of microcracks, and the primary fracture directions are obtained by microcrack orientations. Microcracks are divided into inter-Grain and intra-Grain types, and these two types are further classified into more subtypes according to mineral composition. IG cracks typically form during the pre-peak stage and tend to emerge earlier than TG cracks. Conversely, TG cracks are predominantly created during the post-peak stage but constitute a significant proportion irrespective of flaw inclination angle. A novel method is also proposed to acquire high-stress particles, which promote the identification of dominant macrocrack paths. Finally, the effect of particle size is also systemically analyzed.

Determination of dominant oscillation section in power system based on MSSA algorithm and dissipated energy

The identification of dominant oscillation paths is one of the key aspects in studying the stability of inter-area power grids. This paper proposes an improved multi-channel singular spectrum analysis (MSSA) algorithm combined with dissipative energy to identify dominant oscillation section in power systems. First, an improved MSSA method is introduced, which determines the order using the singular value energy spectrum and can effectively identify inter-area oscillation modes in power systems. Second, an energy function analysis method is proposed, which is well-suited for determining dominant oscillation section in power systems. This method is minimally affected by time-varying and nonlinear factors and allows for the visualization of generator states. Finally, the IEEE 68-bus system is used to construct an equivalent model, demonstrating the method's versatility. Simulation results validate the noise resistance, computational efficiency, and applicability to topology change scenarios of the proposed method. This method can accurately analyze inter-area oscillation modes and help to establish the dominant oscillation section.

Identification of Key Safety Risk Factors and Coupling Paths in Mega Construction Projects

Identification of Key Safety Risk Factors and Coupling Paths in Mega Construction Projects

Enhanced Energy Efficiency through Path Planning for Off-Road Missions of Unmanned Tracked Electric Vehicle

The primary objective of this research is to address the existing gap about the use of a path-planning algorithm that will reduce energy consumption in off-road applications of tracked electric vehicles. The study focuses on examining various off-road terrains and their impact on energy consumption to validate the effectiveness of the proposed solution. To achieve this, a tracked electric vehicle energy model that incorporates vehicle dynamics is developed and verified using real vehicle driving data logs. This model serves as the foundation for devising a strategy that can effectively enhance the energy efficiency of off-road tracked electric vehicles in real-world scenarios. The analysis involves a thorough examination of different off-road terrains to identify strategies that can adapt to diverse landscapes. The path planning strategy employed in this study is a modified version of the A*, called the Energy-Efficient Path Planning (EEPP) algorithm, specifically tailored for the dynamic energy consumption model of off-road tracked electric vehicles. The energy consumption of the produced paths is then compared using the validated energy consumption model of the tracked electric vehicle. It is important to note that the identification of an energy-efficient path heavily relies on the characteristics of the vehicle and the dynamic energy consumption model that has been developed. Furthermore, the algorithm takes into account real-world and practical considerations associated with off-road applications during its development and evaluation process. The results of the comprehensive analysis comparing the EEPP algorithm with the A* algorithm demonstrate that our proposed approach achieves energy savings of up to 6.93% and extends the vehicle’s operational range by 7.45%.

Continuous service-based process monitoring using Pareto analysis and start-end case diagram

PurposeThe purpose of this study is to offer a straightforward, cost-effective, and feasible resolution for managers to assess their processes in a live manner using the process mining technique and to identify anomalies in cases that deviate from the standard. Consequently, the findings of this research can be utilized by organizational managers, while process mining vendors can also leverage it as a feature for their solutions.Design/methodology/approachOur two-step method is designed to initially evaluate the level of standardization within the process, followed by identifying its underlying cause. These two steps are aimed at helping managers effectively evaluate their business processes. The steps are: (1). Start-End Case Diagram: This diagram allows for the evaluation of the lead time trend and identification of cases that deviate from the standard trend line in a service-based process. (2). Happy Path Analysis: Pareto law is suggested to identify the most frequent process variants.FindingsThis approach enables organizations to easily identify problematic cases and investigate bottlenecks when deviations from the standards occur.Originality/valueThe novelty of the paper lies in the introduction and utilization of the start-end case diagram, as well as the combination of this diagram with the Pareto law for the identification of happy path and root cause analysis.

Fractional-Order Least-Mean-Square-Based Active Control for an Electro–Hydraulic Composite Engine Mounts

For the vibration of automobile powertrain, this paper designs electro–hydraulic composite engine mounts. Subsequently, the dynamic characteristics of the hydraulic mount and the electromagnetic actuator were analyzed and experimentally studied separately. Due to the strong nonlinearity of the hybrid electromechanical engine mount, a Fractional-Order Least-Mean-Square (FGO-LMS) algorithm was proposed to model its secondary path identification. To validate the vibration reduction effect, a rapid control prototype test platform was established, and vibration active control experiments were conducted based on the Multiple–Input Multiple–Output Filter-x Least-Mean-Square (MIMO-FxLMS) algorithm. The results indicate that, under various operating conditions, the vibration transmitted to the chassis from the powertrain was significantly suppressed.

Automated Mapping Tool from Moise+ to Colored Petri Nets

The demand for systems incorporating artificial intelligence, such as multi-agent systems, is continually increasing. Simultaneously, there is a growing need for developing tools to support this field, ensuring better fault tolerance within projects. This is particularly crucial given that these systems possess characteristics that render them non-deterministic, thereby amplifying the challenge of conducting tests. To address this challenge, a mapping tool has been developed. This tool automatically generates a graphical model, facilitating the identification of test paths for a given multi-agent system. It operates by taking an XML file from Moise+, an organizational model for multi-agent systems, and translates it into a colored Petri net. The resulting mapping serves as a foundation for generating test cases essential for validating the Moise+ model, guiding system testing. Automation streamlines the process, enhancing speed, and eliminating the potential for human error.

Identification of Main Reaction Path of Soot Formation from Primary Pyrolysis Products in Coal Gasification

Identification of Main Reaction Path of Soot Formation from Primary Pyrolysis Products in Coal Gasification