This paper presents a novel dual tiltrotor UAV design featuring foldable wings and a strategically positioned center of gravity (CG) to enable passive perching and multi-modal flight. Traditional UAVs rely on additional mechanical components for operations such as takeoff and perching, which increase weight and complexity. Inspired by the mechanics of a balanced bird toy, our design achieves stability in horizontal flight and secure power-off perching on branches or cables. The proposed blade-tip plane-based controller facilitates belly/back takeoff without landing gear, enabling seamless transitions between hovering and horizontal flight within 2 seconds. Wind resistance tests were conducted indoors to assess disturbance rejection capabilities during perching, while the transition performance was evaluated outdoors.

Compressors are now widely used in industry and engineering, and blades are one of the most important components in compressors. The performance of the blades directly affects the operating condition and life of the compressor. Currently, the mainstream method for diagnosing and classifying blade faults is based on vibration signal diagnosis. However, traditional methods are limited by the large influence of noise on vibration signals and the singularity of features, and their accuracy and efficiency are relatively low. In addition, as a mainstream diagnostic method, fault diagnosis based on neural networks also suffers from limitations in network structure and data volume, which reduces the generalization of diagnostic methods. Therefore, this paper proposes a new blade fault diagnosis network based on the diffusion model. Specifically, to improve the integrity of the features used for diagnosis, this paper first proposes a learnable weight fusion module and applies it to the fusion process of sound and vibration signals. Secondly, the diffusion model is introduced to generate normal blade signals under corresponding operating conditions when fused features of blades with faults are input. Finally, after obtaining the fused features of normal blades under corresponding operating conditions, the input-output feature difference of the diffusion model is used as the input of the classification network to achieve blade fault diagnosis. In experimental tests, the method proposed in this paper outperforms the current mainstream blade fault diagnosis methods on actual blade fault data. In addition, comparative experiments and ablation experiments also prove the effectiveness of the proposed method.

To ensure the performance of large-scale datacenters, operators need to monitor up to tens of millions of various-type KPIs, e.g., CPU utilization, memory utilization. For each KPI, it is crucial but challenging to detect outliers that deviate from its historical patterns or the patterns of other KPIs in the same period. In this work, we proposeOutSpot, an unsupervised outlier detection framework that integrates hierarchical agglomerative clustering (HAC) with conditional variational autoencoder (CVAE), which significantly improves computational efficiency and comprehensively learns the above two patterns. Additionally, two simple yet effective techniques, soft threshold and median filter, are applied to precisely determine outlier KPIs. Using two real-world datasets collected from the datacenters owned by a top-tier global short video service provider and a top-tier domestic operator,respectively. It demonstrates thatOutSpotachieves the best F1 score of 0.95 and 0.91, AUC of 0.99 and 0.99 on the two datasets, significantly outperforming seven baseline outlier detection methods.

Abstract Grid computing unifies distributed resources via its support for the creation and use of virtual organizations (VOs), where a VO represents a collection of distributed resources to be accessed through predefined resource sharing and coordination policies. We consider a special type of mobile processes, named Grid travelers, which can travel across boundaries of VOs for the detection of resource availability, to negotiate for the approval of access privileges and to conduct remote execution. A new security infrastructure named G‐PASS is proposed to guarantee the validity and integrity of the travelers and the critical security knowledge they collect while traveling, especially while crossing some VOs. G‐PASS borrows the idea of passport and custom, as well as the procedures for people's travel in real life, to provide role‐based delegation mapping and access control. We demonstrate the power and feasibility of G‐PASS with a simulated mobile agent environment and a distributed ray‐tracing application running on multiple VOs. Various security overheads coming from migration decisions and actual agent or process migration are reported. G‐PASS can be installed with Grid Security Infrastructure (GSI) as the base, which makes it compatible with the existing Grid middleware. Copyright © 2006 John Wiley & Sons, Ltd.

This paper presents a grid environment developed for analysis of MRI brain data on global grids. In the current experiment, the MRI data analysis tasks are composed and formulated as a parameter sweep application. This application was deployed using the Gridbus Resource broker. The clusters in our grid environment were managed using PBS and SGE. To execute tasks on remote resources, the Gridbus broker used SSH services for some resources and Globus middleware services for others for initiating execution and management. In this experiment, 16 times 8.1MB images and 28 times 16.3MB images were processed using a volumetric analysis method. The total waiting time was 1800s and the total processing time was 1279.30s. The broker overhead was 521s. Completing this analysis on a single machine could take over twenty hours. This grid architecture provides a useful time-saving analysis tool for neuroimaging applications

Parameter-sweep has been widely adopted in large numbers of scientific applications. Parameter-sweep features need to be incorporated into grid workflows so as to increase the scale and scope of such applications. New scheduling mechanisms and algorithms are required to provide optimized policy for resource allocation and task arrangement in such a case. This paper addresses scheduling sequential parameter-sweep tasks in a fine-grained manner. The optimization is produced by pipelining the subtasks and dispatching each of them onto well-selected resources. Two types of scheduling algorithms are discussed and customized to adapt the characteristics of parameter-sweep, as well as their effectiveness has been compared under multifarious scenarios.

In this paper, an instance-oriented security mechanism is proposed, to attack possible security threats in grid-based mobile agent system. The proposed delegation profile allows application systems to define their own security instances, while it provides mechanisms to delegate one's identity on those instances, instead of on certain hosts, just like the conventional delegation does. This can prevent the delegated host from abusing privileges. By adopting the new delegation profile kernel, a new trust framework is then proposed to enhance the security verification ability and provide more fine-grained authorization to mobile agent platforms.