Dynamic Scenarios Research Articles

Unraveling the sediment routing systems evolution of the south Pyrenean foreland basin during the lower to middle Paleogene period

Orogenesis plays a crucial role in creating, modifying, and disrupting sediment routing fairways. Reconstructing past source to sink systems is a critical step to unravel the geological history, mandatory to disentangle tectonic and climatic forcings, and to understand the perturbations that affected the overall system. The Paleocene-Eocene period is a debated early stage of the early Pyrenean orogeny, characterized by the piggy-back style tectonic partitioning of sub-basins as the deformation front propagates towards the southern foreland. An important issue is to understand how sedimentary systems reacted and reorganized in response to this dynamic scenario. and whether the stratigraphic and sediment provenance results align with the envisioned scenario.In this paper we aim at contributing to the paleogeographic, sedimentologic and tectono-sedimentary evolution of the South-Pyrenean foreland basin by reviewing the chronostratigraphic framework of the basin infill in its south-central sector (the Ager sub-basin in the Serres Marginals thrust sheet). We built five new magnetostratigraphic sections, which together encompass most of the Paleogene record, aimed to best reconcile magnetostratigraphic data with the defined biostratigraphic framework of the region including marine Shallow Benthic Zones (SBZ biozones) and continental mammalian localities (MP levels). Detailed trends in subsidence show the development and evolution of the foreland depozones, from forebulge to foredeep and wedge-top setting, relative to the successive emplacement of the Montsec and Serres Marginals thrust sheets.A correlation with the eastern portion of the foreland basin (Lower-Middle Pedraforca and Cadí Thrust Sheets) and adjacent northern Graus-Tremp basin (Montsec Thrust Sheet) was feasible and seeks to contribute to the tectono-sedimentary evolution of the South-Pyrenean foreland in the light of a source to sink approach. Our proposal includes a new paleogeographic evolution of the area in a series of paleogeographic maps from Late Thanetian to Late Cuisian times.

Mechanisms governing crack speed in peridynamic model

Peridynamic, an innovative solid mechanics method in continuum theory, offers unique advantages for discontinuity modeling such as fracture simulation. This study addressed a puzzling phenomenon observed in peridynamic dynamic fracture modeling, where the predicted normalized limiting crack velocity remained nearly constant across different material parameters input. Delving into the mechanism influencing predicted crack velocity in peridynamic model, we systematically explored the impacts of stiffness distribution of the nonlocal region around the crack tip, through numerical simulations and theoretical analyses. Additionally, the implicit wave velocity degradation in the damage zone of the peridynamic model was uncovered. A prediction formula for the limiting crack velocity was then derived based on these analyses. Furthermore, the wave velocity degradation scheme and the proposed prediction model were validated through numerical instances, involving convergence studies and material density degradation investigation. This work contributes to a further understanding of the peridynamic model and provides new insights for crack velocity prediction in dynamic fracture scenarios.

Precision Targeting Companion: Empowering Marksmen with Smart Rifle Scopes

In the realm of modern warfare, the ability to swiftly identify and engage adversaries with precision is indispensable. This paper outlines our objective to contribute to this paradigm shift by undertaking a comprehensive exploration, and design, of accessories crucial for rifles, including smart scopes, auto- matic triggers, and intelligent magazines. Moreover, this paper introduces a groundbreaking innovation - the Precision Targeting Companion, an intuitive system integrated into rifle scopes, aimed at elevating the effectiveness and accuracy of marksmanship in dynamic combat scenarios. At its core, this system is a testament to the fusion of advanced technology with human ingenuity. By harnessing the power of cutting-edge image processing and predictive analytic, the Precision Targeting Companion offers marksmen a seamless and intuitive interface for detecting potential threats and predicting bullet trajectories in real-time. Yet, beyond its computational prowess, this system embodies a deeper understanding of the challenges faced by soldiers and law enforcement officers on the front-lines. It is a trusted companion, empowering users with the confidence to make split-second decisions in high-pressure situations. Key features of the Precision Targeting Companion include: 1. Swift identification of threats: Leveraging sophisticated image processing algorithms, the system swiftly identifies adver- saries within the user’s line of sight, providing crucial situational awareness. 2. Predictive guidance: Through intricate calculations and analysis, the system predicts bullet trajectories, enabling marksmen to make informed decisions and engage targets with precision. 3. Intuitive assistance: The system’s user-friendly interface overlays visual cues on detected targets and cross-hair alignment, offering intuitive guidance and facilitating seamless target tracking. In essence, the Precision Targeting Companion represents more than just a technological advancement; it is a testament to the enduring partnership between man and machine, working in harmony to safeguard lives and achieve mission success on the battlefield. Keywords: Precision Targeting Companion, Smart Rifle Scopes, Human-Centric Design, Tactical Engagement, Threat Detection, Predictive Guidance.

A Smart Secure model for Detection of DDoS Malicious Traces in Integrated LEO Satellite-Terrestrial Communications

For many researchers, defense against DDoS attacks has always been a major subject of attention. Within the LEO Satellite-Terrestrial (LSTN) network field, distributed denial of service (DDoS) attacks is considered to be one of the most potentially harmful attack techniques. For the facilitation of network protection by the detection of DDoS malicious traces inside a network of satellite devices, machine learning algorithms plays a significant role. This paper uses modern machine learning approaches on a novel benchmark Satellite dataset. The STIN and NSL-KDD datasets has been used to detect network anomalies. The pre-processing of data has been performed effectively and a host of ML methods have been applied to classify the outputs into normal, regular node or untrustable /malicious node. We have evaluated the analysis results in presence of attacks as well as without presence of attacks, supervised machine learning techniques basic measurements like accuracy, True positive, False positive etc. Our proposed trust model shows better accuracy, nearby 98% and we have shown that our proposed machine learning based security model performs better to get rid of DDoS attacks on integrated LEO satellite-terrestrial networks without compromising on the packet routing efficiency. We are able to improve routing speed and improve network security against distributed denial of service (DDoS) attacks by integrating an ensemble-based trust model trained on NSL-KDD+STIN+Exata Simulated resultant dataset with ACO for routing decisions. In dynamic network scenarios, as trustworthiness is an essential criterion in route decision-making, this proposed approach signifies resilient and adaptable routing.

A reliable GTR-PLC approach for power enhancement and online monitoring of solar PV arrays during partial shading

The PV system encounters severe power loss due to the existence of unavoidable partial shading that is mainly overcome by reconfiguration techniques. Besides the advantage of power enhancement, reconfiguration techniques deal with drawbacks such as limited to symmetrical arrays, higher switches and sensors requirements, lower convergence to optimal solution, and improper monitoring. In this paper, a gorilla troop reconfiguration-power line communication (GTR-PLC) approach is proposed for performance enhancement and monitoring of PV arrays during partial shading. The proposed approach uses a considerably lower switch count along with a low-cost simple architecture for loss reduction and smooth operation of the PV array during partial shading. The proposed approach is validated for 5 × 5 and 9 × 9 arrays under time-domain dynamic shading scenarios in simulation whereas real-time hardware setup-based analysis is conducted for 2 × 3 and 12 × 4 arrays using numerous artificial shading cases. The performance of the proposed system is compared with twelve existing static and dynamic techniques and found to have zero misleading power loss with an average power increment of 38.37 %. Also, a power extraction efficiency of 98.99 % and an average calculation time of 0.09 s in the proposed system during partial shading is recorded.

Hyper: A High-Performance and Memory-Efficient Learned Index via Hybrid Construction

Learned indexes use machine learning techniques to improve index construction. However, they often face a fundamental trade-off between performance and memory consumption, especially in dynamic environments with frequent insert and delete operations. This trade-off stems from the construction approaches used in learned indexes: The top-down approach increases performance at the cost of significant memory overhead, while the bottom-up approach focuses on memory efficiency but introduces performance issues due to prediction errors. % A unified solution that simultaneously optimizes performance and memory consumption in dynamic data management scenarios is therefore highly desirable. We propose Hyper, a highly efficient learned index with a novel two-phase hybrid construction approach. Our approach combines bottom-up construction for leaf nodes with top-down construction for inner nodes to achieve an optimal balance between performance and memory consumption. Hyper effectively handles concurrent writes and structure adjustments without sacrificing query performance. We evaluated Hyper on both simple and complex real-world datasets and compared it to seven state-of-the-art learned indexes and several traditional data structures for dynamic workloads. The evaluation results show that Hyper achieves a remarkable performance boost of up to 3.75× with significantly reduced index memory consumption of up to 1610× in the single-thread evaluation. In high concurrency scenarios, Hyper even achieves improvements up to 5.73×, 3.72×, and 3.99× in read-only, read-write, and write-only workloads.

Critical temperature-dependent shear band formation in CoCrNi alloy under high-temperature dynamic compression

In numerous engineering applications, the occurrence of highly rapid loading conditions, coupled with significant temperature rise, is prevalent. Under these harsh conditions, shear localization is a prominent material failure mechanism, which may result in catastrophic failure. Generally, materials possessing higher strength, limited work hardening rate, and lower thermal conductivity are more prone to mechanical instability. However, in the current study, a cryo-rolled CoCrNi alloy, characterized by much higher strength and limited work hardening, shows remarkable room and high-temperature mechanical performance under impact loading except at one temperature domain∼600 °C where this material displays mechanical instability by exhibiting adiabetic shearband. To shed light and understand this abnormal mechanical response, an in-depth microstructural study revealed that the ASB formation is closely linked to the recrystallization behavior of this alloy that is invariantly related to the stored energy of cold work, test temperature, and strain rate. Uniquely, the amplified nano-twinning activity at high strain rate and critical temperature promoted the fragmentation of grains and facilitated recrystallization, respectively. Once the recrystallization is completed, the alloy regains work hardening ability even at very high test temperatures, reaching 900 °C due to the deformation of the newly recrystallized matrix. The present work is valuable in delineating the alloy's application domain for rigorous impact and crash-worthiness applications, as there exists a dearth of literature on this material for dynamic scenarios.

THE REAL TIME HIGH PRECISION CRIMINAL FACE RECOGNITION FOR ATM SECURITY IN DEEP LEARNING

The proposed ATM security system, utilizing composable deep face recognition, emphasizes user protection during transactions with a specific focus on eye retina accuracy. In the event of a potential threat identified through real-time face detection and tracking, an automatic alert is initiated. Prioritizing precision, the system employs advanced face detection algorithms for accurate location and tracking, even in dynamic scenarios. A down sampling technique ensures efficient processing of facial data, crucial for meticulous identification in crowded ATM locations. The integration of a face tracking ID unit enhances accuracy, providing continuous monitoring and persistence in identification, particularly focusing on the distinctive patterns in the eye retina. The scoring method, rooted in face tracking and embedding distance, significantly elevates user identification reliability, minimizing the risk of false positives, especially in criminal identification based on unique eye retina features. In response to potential threats, the system promptly generates automatic alerts and dispatches secure emails to relevant entities, including the affected card user, the respective bank, and cybersecurity authorities. These emails include comprehensive information, such as a high-precision image of the detected criminal's face, detailed facial recognition data, and a thorough summary of their known criminal history. This accuracy-driven and retina focused approach enhances safety, emphasizing continuous refinement and adherence to privacy and ethical considerations during implementation. KEYWORDS: Crime prevention, down-sampling, face recognition.

Designing Empathetic Service Experiences Using Storytelling Approach: A Case Study

Storytelling is a well-known technique to emphasize emotions. This paper explores the integration of storytelling techniques into service design to cultivate empathetic service experiences. Emphasizing the significance of emotional connection in social service contexts, we designed services for Adolescents Living with HIV (ALHIV) in India. Due to a lack of awareness and social stigma, ALHIV are unable to make timely and responsible life decisions around intimacy and HIV status disclosure. We designed services that disseminate essential information and enable ALHIV to make informed life decisions. The solution is an interactive story media where users make decisions around in-design encounters that parallel with possible real-life encounters. As a result, they get to explore multiple possibilities in a safe space, leading to improved real-life decision-making. This paper demonstrates the replicable framework of orchestration of human and technology touchpoints through multiple dynamic and choice-based scenarios. The study examines the synergy between service design and narrative frameworks. Through a detailed case study, the research underscores the pivotal role of storytelling in fostering genuine user engagement by infusing empathy-driven narratives into service encounters. Overall, this research contributes a novel perspective on designing empathetic services, demonstrating the role of storytelling in crafting meaningful service experiences.

Dynamic scenario deduction analysis for hazardous chemical accident based on CNN‐LSTM model with attention mechanism

AbstractThe evolution of hazardous chemical accidents (HCAs) is characterized by uncertainty and complexity. It is challenging for decision‐makers to expeditiously adapt emergency response plans in response to dynamically changing scenario states. This study proposes a data‐driven methodology for constructing accident scenarios and develops a novel hybrid deep learning model for scenario deduction analysis. This model aids in accurately predicting the evolution of HCAs, enabling emergency responders to prepare and implement targeted interventions proactively. First, a framework for constructing an accident scenario database is presented, based on the time‐sequential characteristics of accident progression. This framework employs a data‐driven approach to describe the evolution process of accident scenarios. Second, a deep learning model (CNN‐LSTM‐Attention) that integrates convolutional neural network (CNN), long short‐term memory (LSTM), and attention mechanism (AM) is developed for accident scenario deduction analysis. Finally, to illustrate practical application, a scenario database for HCAs is established. A major HCA case study is conducted to demonstrate the ability of this model to analyze various scenarios, thereby improving emergency decision‐making efficiency. Compared with algorithms such as CNN, LSTM, and CNN‐LSTM, the prediction accuracy of this method ranges from 86% to 93%, signifying an improvement of over 7%. This work provides a reliable framework for supporting decision‐making in emergency management.

Improved performance of a shunt hybrid active power filter by a robust exponential functional link network-based nonlinear adaptive filter control to enhance power quality

This research article details the design and implementation of a nonlinear adaptive filtering (NAF) technique using an exponential functional link network (EFLN) for a shunt hybrid active power filter (SHAPF) control to solve the current-associated power quality issues on the utility side at the distribution level of electrical power systems. Separation of the fundamental component from the harmonics, achieving unity power factor operation, reducing the reactive power drawn from the source, balancing the currents during transients, and reduction of total harmonic distortion (THD) of the source current are the issues considered to resolve. The proposed technique solves these issues by generating the sinusoidal reference current and separating the fundamental current from the harmonics. When compared to conventional and existing adaptive filtering techniques such as least mean square (LMS), least mean fourth (LMF), and variable step size LMS (VSS-LMS), the proposed EFLN-NAF method excels in terms of speedy convergence, adaptability in noise-specific environments and reduced steady-state coefficient error. MATLAB/Simulink software is utilized to perform the simulations to examine the suggested strategy for the chosen SHAPF topology both in static and dynamic scenarios. For a 15 kW and 3kVAr requirement of the nonlinear load, simulation results proved that the designed PPF for 2kVAr is able to share the reactive power with the APF, thereby reducing its rating and cost. The proposed method of filtering has been proven to be fast converging with 0.049 s, and the THD in steady state is brought to 1.32 % in steady-state and to 3.77 % during transient conditions, which are under standard limits. A hardware prototype of the experimental setup is constructed at the laboratory scale with OPAL-RT (OP4510) as the controller. With an active and reactive power demand of 1.1 kW and 210VAr, the designed PPF supplies 110 VAr, whereas the rest is supplied by the APF. The practical THD in source current is observed to be 2.081 %, which meets the standards. The results from both simulations and experiments are validated, and the efficacy of the proposed technique in mitigating the aforementioned power quality issues is proved.

Super-twisting sliding mode controller for energy storage system of a novel multisource hybrid electric vehicle: Simulation and hardware validation

The rapid depletion of fossil fuels, oil, and natural gas due to their excessive use is a source of motivation for finding alternative solutions. Global warming is also an important issue caused by the emission of harmful gases. Consequently, extensive research has been conducted on Fuel Cell-based Hybrid Electric Vehicles (FCEVs) to address the twin challenges of resource depletion and harmful emissions. This study presents a novel model comprising of four sources i.e. Fuel Cell (FC), Photo Voltaic panel (PV), battery, and Supercapacitor (SC). The Proton exchange membrane fuel cell (PEMFC) in which hydrogen is used as fuel is the primary source whereas the other three sources act as secondary sources. These sources are interconnected via DC converters to a DC bus. To regulate this Hybrid Energy Storage System (HESS), a Super-Twisting Sliding Mode Controller (ST-SMC) has been developed, ensuring global stability through the application of Lyapunov criteria. In addition, the proposed controller is compared with a conventional Sliding Mode Controller (SMC) and Integral Sliding Mode Controller (ISMC). Both simulation (MATLAB/Simulink 2021b) and hardware tests (MicroLabBox dSPACE RTI1202) confirm the system's stability, resilience, and efficient functionality across various dynamic scenarios. Comparative analysis between the three demonstrates the superiority of ST-SMC over the SMC and ISMC, as verified by the results.

MOLO-SLAM: A Semantic SLAM for Accurate Removal of Dynamic Objects in Agricultural Environments

Visual simultaneous localization and mapping (VSLAM) is a foundational technology that enables robots to achieve fully autonomous locomotion, exploration, inspection, and more within complex environments. Its applicability also extends significantly to agricultural settings. While numerous impressive VSLAM systems have emerged, a majority of them rely on static world assumptions. This reliance constrains their use in real dynamic scenarios and leads to increased instability when applied to agricultural contexts. To address the problem of detecting and eliminating slow dynamic objects in outdoor forest and tea garden agricultural scenarios, this paper presents a dynamic VSLAM innovation called MOLO-SLAM (mask ORB label optimization SLAM). MOLO-SLAM merges the ORBSLAM2 framework with the Mask-RCNN instance segmentation network, utilizing masks and bounding boxes to enhance the accuracy and cleanliness of 3D point clouds. Additionally, we used the BundleFusion reconstruction algorithm for 3D mesh model reconstruction. By comparing our algorithm with various dynamic VSLAM algorithms on the TUM and KITTI datasets, the results demonstrate significant improvements, with enhancements of up to 97.72%, 98.51%, and 28.07% relative to the original ORBSLAM2 on the three datasets. This showcases the outstanding advantages of our algorithm.

Flower visitor networks: Reduced reproductive success driven by antagonists in a hummingbird‐pollinated species

AbstractFlower–animal interactions encompass a continuum of mutualistic to antagonistic behaviors. Although the importance of pollinators in interaction networks has been widely reported, the role of floral antagonists remains overlooked. In Collaea cipoensis, an endemic shrub from southeastern Brazil, we modeled an individual‐based network assessing the flower visitors' functional behavior and tested if plant centrality increases fitness. Centrality indicates how well‐connected a given plant is with the rest of the conspecifics through shared flower visitors. Specifically, we tested if flower visitor interactions are amplified by many‐flowered individuals, and whether the relationship between centrality and plant fitness encompasses the effects of mutualists or antagonists. Subsequently, we quantified each visiting species' effectiveness. Antagonistic interactions were the most frequent in the network (97% of visits), primarily conducted by the nectar‐robbing bee Trigona spinipes (66%), which was also the most central species and detrimental antagonist. The hummingbirds Colibri serrirostris and Eupatomena macroura were the most efficient pollinators, with a high contribution to the quantity—floral visitation—and the quality—viable seeds—components, respectively. Many‐flowered plants showed greater centrality, which boosted the plant's cohesion in the network and increased flower visitation. Accordingly, many‐flowered plants were better connected, mainly due to antagonistic visitors. Thus, centrality increases were not positively translated into more reproductive success. Our results reinforce the dynamic scenarios of flower–animal interactions, with consequences to plant reproductive ecology. We highlight the importance of incorporating distinct types of interactions to unveil the complexity of interacting systems, that when not explored, hide potential evolutionary consequences for plant reproductive success.Abstract in Portuguese is available with online material.

Toward the full implementation of the water-energy-food nexus in computable general equilibrium modelling: methods and macroeconomic implications

ABSTRACT This paper contributes to the advancement of Computable General Equilibrium (CGE) modelling in addressing the Water-Energy-Food (WEF) Nexus. As such, it introduces water resources as a production factor for both the energy sector and irrigated agriculture, as well as their competition for the endowment, aiming to explicitly represent additional components of the WEF with respect to a standard CGE in the literature. Thus, it develops different modelling structures by computing impacts on regional GDP, sectorial prices, and production outputs in response to hypothetical water scarcity scenarios. This analysis allows for the determination of the role of data and modelling assumptions, such as production function, water substitutability with other endowments, water mobility across sectors, and sectorial water intensity, in influencing the results. Finally, the paper develops a dynamic scenario analysis, showing that an enhanced representation of the Nexus can significantly affect the macroeconomic dynamics of the simulations and their regional implications.

Speech emotion recognition in real static and dynamic human-robot interaction scenarios

The use of speech-based solutions is an appealing alternative to communicate in human-robot interaction (HRI). An important challenge in this area is processing distant speech which is often noisy, and affected by reverberation and time-varying acoustic channels. It is important to investigate effective speech solutions, especially in dynamic environments where the robots and the users move, changing the distance and orientation between a speaker and the microphone. This paper addresses this problem in the context of speech emotion recognition (SER), which is an important task to understand the intention of the message and the underlying mental state of the user. We propose a novel setup with a PR2 robot that moves as target speech and ambient noise are simultaneously recorded. Our study not only analyzes the detrimental effect of distance speech in this dynamic robot-user setting for speech emotion recognition but also provides solutions to attenuate its effect. We evaluate the use of two beamforming schemes to spatially filter the speech signal using either delay-and-sum (D&S) or minimum variance distortionless response (MVDR). We consider the original training speech recorded in controlled situations, and simulated conditions where the training utterances are processed to simulate the target acoustic environment. We consider the case where the robot is moving (dynamic case) and not moving (static case). For speech emotion recognition, we explore two state-of-the-art classifiers using hand-crafted features implemented with the ladder network strategy and learned features implemented with the wav2vec 2.0 feature representation. MVDR led to a signal-to-noise ratio higher than the basic D&S method. However, both approaches provided very similar average concordance correlation coefficient (CCC) improvements equal to 116 % with the HRI subsets using the ladder network trained with the original MSP-Podcast training utterances. For the wav2vec 2.0-based model, only D&S led to improvements. Surprisingly, the static and dynamic HRI testing subsets resulted in a similar average concordance correlation coefficient. Finally, simulating the acoustic environment in the training dataset provided the highest average concordance correlation coefficient scores with the HRI subsets that are just 29 % and 22 % lower than those obtained with the original training/testing utterances, with ladder network and wav2vec 2.0, respectively.

Overhead and Performance of Dynamic Network Slice Allocation for Mobile Users

Based on virtualisation technologies like SDN and NFV, modern networks like 5G create customised logical networks known as Network Slicing to serve distinct user groups. The mobility of a significant part of these users brings great challenges for that scenario, e.g. demand variations and continuous connectivity. In such a scenario, the dynamic resource allocation of these slices is shown as a possible solution. However, that approach may be sensitive to computing overhead and trade-off scenarios, which are not fully evaluated in the literature. This work focuses on that gap by researching the impact of network slice reconfiguration overhead on the overall network performance. An extensive evaluation of Dynamic Network Slicing in providing user mobility support in Fog computing-based architectures is presented. This paper presents a resource allocation model that calculates the required time to allocate a slice instance. Furthermore, based on those values, this work presents an evaluation of the impact of resource allocation delays in terms of bandwidth allocation, latency, and service outage in different dynamic slicing scenarios that include service migration. Simulations based on realistic values point out that reducing the allocation delay from 60 to 30 s can lead the network to 25% more stable latency to its users in 60 min slice lifetime.

Turing-like Experiment in a Cyber Defense Game

During the past decade, researchers of behavioral cyber security have created cognitive agents that are able to learn and make decisions in dynamic environments in ways that assimilate human decision processes. However, many of these efforts have been limited to simple detection tasks and represent basic cognitive functions rather than a whole set of cognitive capabilities required in dynamic cyber defense scenarios. Our current work aims at advancing the development of cognitive agents that learn and make defense-dynamic decisions during cyber attacks by intelligent attack agents. We also aim to evaluate the capability of these cognitive models in ``Turing-like'' experiments, comparing the decisions and performance of these agents against human cyber defenders. In this paper, we present an initial demonstration of a cognitive model of the defender that relies on a cognitive theory of dynamic decision-making, Instance-Based Learning Theory (IBLT); we also demonstrate the execution of the same defense task by human defenders. We rely on OpenAI Gym and CybORG and adapt an existing CAGE scenario to generate a simulation experiment using an IBL defender. We also offer a new Interactive Defense Game (IDG), where \textit{human} defenders can perform the same CAGE scenario simulated with the IBL model. Our results suggest that the IBL model makes decisions against two intelligent attack agents that are similar to those observed in a subsequent human experiment. We conclude with a description of the cognitive foundations required to build autonomous intelligent cyber defense agents that can collaborate with humans in autonomous cyber defense teams.

A Framework of Grasp Detection and Operation for Quadruped Robot with a Manipulator

Quadruped robots equipped with manipulators need fast and precise grasping and detection algorithms for the transportation of disaster relief supplies. To address this, we developed a framework for these robots, comprising a Grasp Detection Controller (GDC), a Joint Trajectory Planner (JTP), a Leg Joint Controller (LJC), and a Manipulator Joint Controller (MJC). In the GDC, we proposed a lightweight grasp detection CNN based on DenseBlock called DES-LGCNN, which reduced algorithm complexity while maintaining accuracy by incorporating UP and DOWN modules with DenseBlock. For JTP, we optimized the model based on quadruped robot kinematics to enhance wrist camera visibility in dynamic environments. We integrated the network and model into our homemade robot control system and verified our framework through multiple experiments. First, we evaluated the accuracy of the grasp detection algorithm using the Cornell and Jacquard datasets. On the Jacquard dataset, we achieved a detection accuracy of 92.49% for grasp points within 6 ms. Second, we verified its visibility through simulation. Finally, we conducted dynamic scene experiments which consisted of a dynamic target scenario (DTS), a dynamic base scenario (DBS), and a dynamic target and base scenario (DTBS) using an SDU-150 physical robot. In all three scenarios, the object was successfully grasped. The results demonstrate the effectiveness of our framework in managing dynamic environments throughout task execution.

Maximal size constraint community search over bipartite graphs

Community search over bipartite graphs is a crucial issue with significant applications in various domains, including group recommendations, fraud detection and representation learning. Current research primarily focuses on the structural cohesiveness constraint between two sets of vertices, often neglecting the community’s size constraint. Unfortunately, this oversight may lead to communities of sizes ranging from excessively small to substantially large, thereby affecting their applicability. Therefore, in this paper, we focus on the maximal size constraint community search over bipartite graphs, which not only takes into account the (α,β) degree constraints for each set of vertices but also ensures that the number of vertices in each set is limited to ξ and ζ, respectively. To solve this problem, we first introduce two competitive algorithms, namely Ordering-based Expansion Algorithm (OEA) and Followers-based Peeling Algorithm (FPA), wherein they greedily expand or peel vertices, respectively, to identify the maximal one in a breadth-first manner, utilizing a novel vertex ordering and followers concept. To further accelerate the computation, we propose an advanced Expand-and-Filter Algorithm (EFA++) that first acquires a slightly larger community and subsequently executes a filtering phase employing a dynamic programming technique. Furthermore, we define the “Affected Area” for edge insertion or deletion that caters for dynamic scenarios. Finally, the theoretical analysis and extensive experimental evaluation on several real-life datasets demonstrate the effectiveness and efficiency of the proposed algorithms.