Reselection Mechanism Research Articles

Open-world object detection: A solution based on reselection mechanism and feature disentanglement

Traditional object detection algorithms operate within a closed set, where the training data may not cover all real-world objects. Therefore, the issue of open-world object detection has attracted significant attention. Open-world object detection faces two major challenges: “neglecting unknown objects” and “misclassifying unknown objects as known ones.” In our study, we address these challenges by utilizing the Region Proposal Network (RPN) outputs to identify potential unknown objects with high object scores that do not overlap with ground truth annotations. We introduce the reselection mechanism, which separates unknown objects from the background. Subsequently, we employ the simulated annealing algorithm to disentangle features of unknown and known classes, guiding the detector’s learning process. Our method has improved on multiple evaluation metrics such as U-mAP, U-recall, and UDP, greatly alleviating the challenges faced by open world object detection.

Energy-aware virtual machine placement based on a holistic thermal model for cloud data centers

As energy-intensive infrastructures, data centers (DCs) have become a pressing challenge for managers due to their significant energy consumption and carbon emissions. Information technology (IT) and cooling systems contribute the most to energy consumption. Energy-aware virtual machine (VM) scheduling methods have been widely demonstrated to reduce energy consumption and operating costs in DCs. However, as realistic DCs exhibit complex power and thermodynamic behaviors, existing works cannot provide efficient measures to optimize computing and cooling power consumption simultaneously. To overcome this challenge, we construct a holistic thermal model (including CPU and server inlet thermal models) to accurately represent the non-uniform, dynamic thermal environment. Subsequently, this work proposes a thermal model-based energy-aware VM placement method (TEVP) to minimize the holistic energy consumption of the DCs, considering resource and thermal constraints. We develop a novel hybrid swarm intelligence algorithm (DE-ERPSO) combining differential evolution (DE) and particle swarm optimization with an elite re-selection mechanism (ERPSO) to explore more energy-efficient VM placement schemes. Extensive experiments are conducted on an extended CloudSim to validate the performance of the proposed TEVP using real-world workload traces (PlanetLab and Azure). Results show that TEVP saves over 5.6% of the total energy consumption over the advanced baselines while maintaining low thermal violations.

Weighted envelope spectrum based on reselection mechanism and its application in bearing fault diagnosis

As a powerful algorithm, fast spectral correlation (Fast-SC) is widely used in bearing fault diagnosis. However, the interference of strong background noise and the existence of multiple fault-related frequency bands make it difficult to diagnose bearing faults using enhanced envelope spectrum and improved envelope spectrum on the basis of Fast-SC. To alleviate the problem, fast kurtogram is imitated to divide the spectral frequency bands; then, the threshold-based diagnostic feature (TDF) of each integration band is calculated to guide the weighting function of the corresponding level; finally, weighted envelope spectrum with the largest TDF is determined as the output; this process is called the reselection mechanism. Simulation and experimental results indicate that the design of weighting function highlights the contribution of the fault-related frequency bands, and the reselection mechanism further improves its performance.

Vehicle-to-Everything (V2X) Evolution From 4G to 5G in 3GPP: Focusing on Resource Allocation Aspects

Cellular Vehicle-to-everything (V2X) is evolving from a communication technology for safety messages towards a platform for advanced V2X services such as platooning and autonomous driving. The development of technical specifications for V2X communications is done across several task specification groups (TSGs) and working groups (WGs) within 3rd Generation Partnership Project (3GPP). In this paper, we focus on radio access network (RAN) aspects for direct communication between user equipments (UEs), which is known as sidelink (SL) communication. Resource allocation for SL communication can be performed in one of two modes. In the first mode, resource allocation is centralized in the network. In the second mode, resource allocation is performed in a distributed manner among UEs, where resource selection is performed at a transmitting UE, i.e., the transmitting UE determines time and frequency resources for a SL transmission without the assistance of a base station. In this paper, we review V2X evolution from 4G to 5G in 3GPP focusing on resource allocation aspects defined in Long-Term Evolution (LTE) and New Radio (NR) specifications, with particular attention given to the difference in design and performance of each. We highlight the key 3GPP standard developments on V2X resource allocation which includes enhanced resource reselection mechanisms and partial sensing schemes. It is observed that NR V2X resource reselection mechanism can provide up to 20% of reliability improvement over LTE V2X and NR V2X partial sensing scheme can provide up to 95% of reduced power consumption over full sensing scheme.

A Novel V2V-Based Temporary Warning Network for Safety Message Dissemination in Urban Environments

Vehicular communication networks (VCNs) have been widely recognized as promising solutions to support safety-related applications in urban transportation systems. However, constructing and maintaining such networks is quite challenging due to the complex traffic and communication environment. Substantial studies have focused on the design of the networking schemes and message dissemination protocols. Nonetheless, most existing designs only consider the connectivity and rapid end-to-end transmission, regardless of the network coverage and duration. In this article, we propose a novel temporary warning network (TWN) for safety message dissemination in the urban traffic environment, in which both the spatial distribution and temporal duration of the networking scheme are taken into account. Specifically, TWN is constructed by the selection of relay vehicles based on the spatiotemporal correlation of vehicle trajectory so that the safety message can be quickly disseminated within the Regions of Interest (RoIs). To maintain TWN during an accident, a reselection mechanism is also proposed, which enables newly come vehicles in the RoI to receive the messages in time. Finally, we conduct extensive numerical experiments to validate the effectiveness of our method in various traffic scenarios.

An extended ACO-based mobile sink path determination in wireless sensor networks

In wireless sensor networks (WSNs), a mobile sink accumulate the data instead of routing directly to the sink to avoid the hotspot problem. In this process, it traverses a predetermined path by visiting a set of nodes called the rendezvous point (RP), and all the non-rendezvous points can transmit their data to the closest RP. Identifying the best collection of RPs and determining the mobile sink traveling path will decrease data loss and improve network performance. However, choosing a set of RPs and the route between them is a challenging task. It is more complicated in the event-driven applications due to the uneven data rate of SNs. In this context, we propose an extended ant colony optimization (ACO)-based mobile sink path construction for event-driven WSNs. In this, the best set of the RPs and the efficient mobile sink traveling path between them is determined. In addition to this, the RPs re-selection mechanism also adopted for balancing the energy between the nodes. After that, the virtual RPs are introduced to minimize the data transmissions between the sensor nodes and RPs. This process will improve WSNs’ performance in terms of reducing data losses while increasing network lifetime. The improved performance of the extended ACO-MSPD over existing is confirmed through simulation tests.

Autonomous Robotic Exploration by Incremental Road Map Construction

In this paper, we propose a novel path planning framework for autonomous exploration in unknown environments using a mobile robot. A graph structure is incrementally constructed along with the exploration process. The structure is the road map that represents the topology of the explored environment. To construct the road map, we design a sampling strategy to get random points in the explored environment uniformly. A global path from the current location of the robot to the target area can be found on this road map efficiently. We utilize a lazy collision checking method that only checks the feasibility of the generated global path to improve the planning efficiency. The feasible global path is further optimized with our proposed trajectory optimization method considering the motion constraints of the robot. This mechanism can facilitate the path cost evaluation for the next best view selection. In order to select the next best target region, we propose a utility function that takes into account both the path cost and the information gain of a candidate target region. Moreover, we present a target reselection mechanism to evaluate the target region and reduce the extra path cost. The efficiency and effectiveness of our approach are demonstrated using a mobile robot in both simulation and real experimental studies. Note to Practitioners —This paper is motivated by the efficient exploration problem, which plays a key role in various areas such as the information gathering and environment monitoring. In these applications, the mission length and executing time are often restricted by the battery capacity of the robot. In this paper, an efficient path planning framework is introduced to reduce the path length and exploration time. The robot keeps a road map of the environment to facilitate the path planning. The proposed target selection mechanism helps the robot determine the next best target to explore. Furthermore, the proposed trajectory optimization algorithm helps in reducing the path cost. Overall, this framework enables efficiently and autonomously exploration with a novel path planning framework.

An expanded particle swarm optimization based on multi-exemplar and forgetting ability

There are two phenomena in human society and biological systems. One is that people prefer to extract knowledge from multiple exemplars to obtain better learning ability. The other one is the forgetting ability that helps the encoding and consolidation of new information by removing unused or unwanted memories. Inspired by these phenomena, this paper transplants the multi-exemplar and forgetting ability to particle swarm optimization (PSO), and proposes an eXpanded PSO, called XPSO. Firstly, XPSO expands the “social-learning” part of each particle from one exemplar to two exemplars, learning from both the locally and the globally best exemplars. Secondly, XPSO assigns different forgetting abilities to different particles, simulating the forgetting phenomenon in the human society. Under the multi-exemplar learning model with forgetting ability, XPSO further adopts an adaptive scheme to update the acceleration coefficients and selects a reselection mechanism to update the population topology. The effectiveness of these additional proposed strategies is verified by extensive experiments. Moreover, comparison results among XPSO and other 9 popular PSO as well as 3 non-PSO algorithms on CEC’13 test suite suggest that XPSO attains a very promising performance for solving different types of functions, contributing to both higher solution accuracy and faster convergence speed.

Beam Management and Self-Healing for mmWave UAV Mesh Networks

The point-to-point and point-to-multipoint communication features in the IEEE 802.11ay make it possible for unmanned aerial vehicles (UAVs) to form a UAV mesh network (flyMesh). In an 802.11ay based millimeter-wave (mmWave) flyMesh, many challenges are brought by the collaborative interactions of UAVs and the beam misalignment of the directional communication links. This paper studies how to guarantee the robustness of the mmWave flyMesh with respect to beam management and network self-healing. First, to cope with the problem of beam misalignment between UAVs or between the UAV group leader and relay base station/satellite caused by the movement of UAV(s), we propose a fast beam tracking mechanism. Second, in order to solve the problem of link failures due to departures of some UAVs from the group or failures, the self-healing mechanism is proposed to find alternative links to restore the network capability. Finally, to deal with the dynamic group leader changes of UAVs due to mobility, we propose an efficient UAV group leader re-selection mechanism to reduce the overhead of UAV group management. Through performance analysis and simulation, we demonstrate that our proposed mechanisms can effectively address the aforementioned problems and challenges.

Measurement threshold configuration scheme based on the traffic load

This study proposes a performance analysis of the measurements mechanism applicable to the terminal controlled cell reselection algorithm in a mobile cellular communication system. The cell reselection mechanism decides on which cell, a user equipment (UE) is camped on, when it is in the idle mode. The analysis demonstrates the impact of the neighbour cells measurement threshold setting during the reselection on the serving cell pilot channel quality and a measurement effort in the UE, in terms of the time spent measuring neighbour cells. The authors demonstrate that there is a trade-off between the cell reselection performance, in terms of the signal quality during the mobility and the battery life in the mobile terminal with different system configurations. Furthermore, a novel approach and enhanced load based measurement threshold configuration scheme is proposed, in order to improve this trade-off and also the overall system performance and the user experience. Performance analysis results are given in different scenarios to demonstrate the efficacy of the proposed scheme.

Adaptive Dynamic Composite Service Reselection Mechanism with Compensation

Adaptive Dynamic Composite Service Reselection Mechanism with Compensation

Cluster Formation of Wireless Sensor Nodes using Adaptive Particle Swarm Optimisation

Hierarchical based clustering protocol for wireless sensor network is suitable to use in energy efficient environmental monitoring. In clustering protocol, sensor nodes that are cluster heads (CHs) have to collect information from cluster member and transmit to the base station. Strategic CHs location can significantly affect the network overall energy consumption. Therefore, selecting suitable CHs location becomes a challenging task. In this work, CHs distribution using adaptive particle swarm optimisation (APSO) is proposed. Particle swarm optimization (PSO) is one of the swarm intelligence methods that is designed to search for optimum solution by mimicking the behavior of bird flocking and fish schooling. Introduction of adaptive cognitive and social learning factor can achieve better convergence speed and particles reselection mechanism to reduce the chances of getting trapped at local maximum. The performance of the proposed method is compared with the low energy adaptive cluster hierarchical (LEACH) protocol. Simulation results show that the proposed method outperforms LEACH in terms of first node dies (FND) round, total data received at the base station and energy consumed per round.

A self-optimizing QoS-aware service composition approach in a context sensitive environment

QoS-aware service composition is aimed to maximize the global QoS of a composite service when selecting candidate services. In a context sensitive service execution environment in pervasive computing, the context information for service composition is not static: device, policy, and user constraints, and QoS requirements may change, new services may be deployed, old ones withdrawn, or existing ones change their QoS parameters. This results in the current service composition plan failing or its QoS degrading from the optimum. In this paper, a runtime self-optimizing service composition framework is proposed. An implementation of a prototype for this framework is presented, addressing the issues of reducing extra delay while increasing global QoS in service composition in a dynamic context environment. Three service re-plan algorithms are compared that can be used in dynamic context environment, i.e., minimal-conflict hill-climbing repair genetic algorithm (MCHC-repair GA), an improved penalty-based GA, and our multi-population conflicts sorted repair genetic algorithm (MP-CS-repair GA), as well as three kinds of service composition mechanisms-with backup, without backup, and our context-aware service re-selection mechanisms. The results show that our MP-CS-repair GA and context-aware service re-selection method can reduce more extra delay while acquiring a higher global QoS for the composite service in a context sensitive environment. This context-aware service re-selection mechanism also shows some adaptability to different context change frequencies and user requirements for reducing computation cost in the self-optimizing process.

Scapegoating Strategically: Reselection, Strategic Interaction, and the Diversionary Theory of War

Proponents of the diversionary theory of war have often argued that domestic reselection incentives induce office-seeking leaders to pursue aggressive foreign policies. To examine the relationship between strategic interaction and diversionary incentives, this article develops a two-state, two-sided incomplete information deterrence model with domestic reselection. According to the model, reselection mechanisms increase a leader's propensity to pursue aggressive foreign policies. Diversionary incentives in attacking states lead to an increase in war outcomes, while diversionary incentives in defending states may or may not increase the probability of war. The model also predicts that there will be nonmonotonic relationships between economic performance and war and between regime type and diversionary behavior, which may explain the discrepancies among many empirical tests of diversionary theory.