Targeted Attacks on Object Detection (TAOD) aim to deceive the victim detector into recognizing a specific instance as the predefined target category while minimizing the changes to the predicted bounding box of that instance. Yet, this kind of flexible attack paradigm, which is capable of manipulating the decision outcome of the victim detector, received limited attention, especially in the context of attacking object detection in optical remote sensing images, where relevant research remains a blank. To fill this gap, this paper concentrates on TAOD in optical remote sensing images, and pays attention to a fundamental question, how to deploy TAOD via the raw predictions (the predictions before non-maximum suppression) of a victim detector. In this regard, we depart from widely adopted task-independent importance measurements and hard-weighted ensemble optimization schemes present in existing methods. Instead, we first define the task-specific importance score, which considers both the qualities and the attack costs of predictions. Further, we propose the Task-Specific Importance-Aware Candidate Predictions Selection Scheme (TSIA-CPSS) alongside the Soft-Weighted Ensemble Optimization Scheme (SW-EOS). A total of eleven detectors on DIOR and DOTA, two commonly employed benchmarks, are included to comprehensively evaluate our approach. Furthermore, we indicate that the effectiveness of our approach is not only substantial for vanilla TAOD, but also can be better generalized to extended scenarios, which encompasses random TAOD, TAOD on oriented object detection, and targeted patch attacks, highlighting the noteworthy potential of our approach. Our codes will be released on Github.

The detection of oblique wedge is a crucial task in the detection and repair of railway freight car bogies, which involves multiple inspection items. Among them, the wear degree of rubbing surface directly affects the damping effect and stability of the train, which is a very important inspection item. the exiting method is the manual measurement that uses the non-standard measuring tools and visual inspection. which cannot achieve the ever-increasing measurement efficientcy and accuracy of automate maintenance. In order to solve this problem, the measurement method and algorithm based on point cloud are researched to measure the wear of the rubbing surface. The test results shows that the method is superior to the existing manual measurement method in terms of accuracy and efficiency, and provides important technical support for the automatic maintenance and upgrading of the oblique wedge.

Based on that the normal lines of points on the same latitude of a surface of revolution point to the same point on the axis, a strategy is proposed to find the axis of the surface of revolution by fitting the intersections of normal lines. The k-d tree is used in this paper to address the computational challenges. The shortest distance between the normal lines of two points in the neighborhood of a certain point is calculated to determine whether the intersection of normal lines needs to be calculated. The RANSAC algorithm is used to fit the line to the normal lines intersections to obtain the axis. The axis extraction method is tested on cone, cylinder, spindle, and incomplete shapes of them. the error of extracted axis is small, indicating the feasibility of this method.

Based on the state-space representation of inertia wheel pendulum, Euler method is used to update the state of inertia wheel pendulum system. The effect of actor-critic algorithm on inertia wheel pendulum system control is studied with different sample time and different proportion of angle in reward. The results show that the training effect is better with smaller sample time. Because the main goal of inertia wheel pendulum control is to control the angle of the pendulum, more weight should be given to the angle in reward to make the training to be stable more easily.

Aiming at the current high energy consumption problem of the Internet of Things data center, and the static state of the traditional data center network architecture is not convenient to the design and deployment of energy consumption optimization strategies, this paper specifically proposes a new energy consumption optimization strategy under the Software Defined Network (SDN) architecture, the control plane is separated from the data plane. The control plane is divided into two layers, bottom layer and top layer. The bottom layer controllers are uniformly scheduled by the upper layer controller. In order to reduce the computational pressure of the upper-level controller and make full use of the computing power of the bottom level controller, the relationship between the bottom layer controllers in this article is non-cooperative. On this basis, two energy efficiency strategies are designed in this paper. The first one is Strategy For Nash Equilibrium Point (SFNEP). The second one is Strategy For Energy Optimal(SFEO). The target of SFNEP is to search for the Nash Equilibrium Point. The target of SFEO is to gain energy efficiency via running SFNEP. Through extensive simulations, we show that SFEO has a better energy consumption optimization effect than other benchmark strategies. Under ideal condition, energy saving ratio is able to reach 24%.