Contextual Hierarchical Part-Driven Conditional Random Field Model for Object Category Detection

Even though several promising approaches have been proposed in the literature, generic category-level object detection is still challenging due to high intraclass variability and ambiguity in the appearance among different object instances. From the view of constructing object models, the balance between flexibility and discrimination must be taken into consideration. Motivated by these demands, we propose a novel contextual hierarchical part-driven conditional random field (CRF) model, which is based on not only individual object part appearance but also model contextual interactions of the parts simultaneously. By using a latent two-layer hierarchical formulation of labels and a weighted neighborhood structure, the model can effectively encode the dependencies among object parts. Meanwhile, beta-stable local features are introduced as observed data to ensure the discriminative and robustness of part description. The object category detection problem can be solved in a probabilistic framework using a supervised learning method based on maximum a posteriori (MAP) estimation. The benefits of the proposed model are demonstrated on the standard dataset and satellite images

Architecture Design and Performance Analysis of Supervisory Control System of Multiple UAVs

Although UAV systems are currently controlled by a group of people, in the future, increased automation could allow a single operator to supervise multiple UAVs. Operators will be involved in the mission planning, imagery analysis, weapon control, and contingency interventions. This study examines the architecture and prototype of multiple UAVs supervisory control system. Firstly, the architecture for testing and evaluating human supervisory system controlling multiple UAVs is devised and each sub-system is described in detail. Then a prototype test bed of multiple UAVs supervisory control for demonstrating architecture and adaptive levels of autonomy is built. Finally, with the test bed, the impact of dynamic role allocation on system performance is studied based on quantitative criteria of wait times and operator utilisation. It is shown by simulation that dynamic role allocation can effectively shorten wait times, and eventually improve the system performance.Defence Science Journal, Vol. 65, No. 2, March 2015, pp.93-98, DOI:http://dx.doi.org/10.14429/dsj.65.583

Cooperative Search by Multiple Unmanned Aerial Vehicles in a Nonconvex Environment

This paper presents a distributed cooperative search algorithm for multiple unmanned aerial vehicles (UAVs) with limited sensing and communication capabilities in a nonconvex environment. The objective is to control multiple UAVs to find several unknown targets deployed in a given region, while minimizing the expected search time and avoiding obstacles. First, an asynchronous distributed cooperative search framework is proposed by integrating the information update into the coverage control scheme. And an adaptive density function is designed based on the real-time updated probability map and uncertainty map, which can balance target detection and environment exploration. Second, in order to handle nonconvex environment with arbitrary obstacles, a new transformation method is proposed to transform the cooperative search problem in the nonconvex region into an equivalent one in the convex region. Furthermore, a control strategy for cooperative search is proposed to plan feasible trajectories for UAVs under the kinematic constraints, and the convergence is proved by LaSalle’s invariance principle. Finally, by simulation results, it can be seen that our proposed algorithm is effective to handle the search problem in the nonconvex environment and efficient to find targets in shorter time compared with other algorithms

Can the digital economy promote the development of the energy economy? Evidence from China

In this paper, 22 indexes are selected at three levels, including the informatization development level, the Internet development level, and the digital transaction development level, based on China’s provincial panel data from 2011 to 2020, so as to build a digital economy development index system. Moreover, 28 basic indexes are selected from three aspects, including energy construction, energy production and energy consumption, so as to develop an energy economy development evaluation index system. The development index of China’s digital economy and energy economy are measured by using the entropy weight method. The effect of the digital economy on the energy economy and its mechanism are tested by the static panel, the dynamic panel, and the mediating effect and regulating effect models. The results indicate that the digital economy has pronouncedly promoted the development of China’s energy economy, and the development of the digital economy can have an effect on the rationalization of the industrial structure and then affect the development of the energy economy, and there is an intermediary effect. Moreover, the upgrading of the industrial structure is conducive to regulating the digital economy and facilitates the development of the energy economy. The development of the energy economy can be better promoted by focusing on the coordinated regional layout of the digital economy development, building a reliable energy commodity trading platform, and expediting the optimization and upgrading of the industrial structure

Magnetic field and torque output of packaged hydraulic torque motor

Hydraulic torque motors are one key component in electro-hydraulic servo valves that convert the electrical signal into mechanical motions. The systematic characteristics analysis of the hydraulic torque motor has not been found in the previous research, including the distribution of the electromagnetic field and torque output, and particularly the relationship between them. In addition, conventional studies of hydraulic torque motors generally assume an evenly distributed magnetic flux field and ignore the influence of special mechanical geometry in the air gaps, which may compromise the accuracy of analyzing the result and the high-precision motion control performance. Therefore, the objective of this study is to conduct a detailed analysis of the distribution of the magnetic field and torque output; the influence of limiting holes in the air gaps is considered to improve the accuracy of both numerical computation and analytical modeling. The structure and working principle of the torque motor are presented first. The magnetic field distribution in the air gaps and the magnetic saturation in the iron blocks are analyzed by using a numerical approach. Subsequently, the torque generation with respect to the current input and assembly errors is analyzed in detail. This shows that the influence of limiting holes on the magnetic field is consistent with that on torque generation. Following this, a novel modified equivalent magnetic circuit is proposed to formulate the torque output of the hydraulic torque motor analytically. The comparison among the modified equivalent magnetic circuit, the conventional modeling approach and the numerical computation is conducted, and it is found that the proposed method helps to improve the modeling accuracy by taking into account the effect of special geometry inside the air gaps

Wavelet Denoising Using the Pareto Optimal Threshold

The denoising of a natural image corrupted by noise is a classical problem in image processing. In this paper, an efficient algorithm of image denoising based on multi-objective optimization in discrete wavelet transform (DWT) domain is proposed, which can achieve the Pareto optimal wavelet thresholds. First, the multiple objectives for image denoising are presented, then the relation between these objectives and the wavelet thresholds is analysed, finally the algorithm of adaptive multi-objective particle swarm optimization is introduced to optimize the wavelet thresholds. Experiments show that the Pareto optimal threshold-denoising algorithm is more effective than other algorithms, and can attain the Pareto optimal denoised image. Key words: Image denoising, multi-objective optimization, particle swarm optimization, discrete wavelet transform, thresholding function. 1

Reusing Source Task Knowledge via Transfer Approximator in Reinforcement Transfer Learning

Transfer Learning (TL) has received a great deal of attention because of its ability to speed up Reinforcement Learning (RL) by reusing learned knowledge from other tasks. This paper proposes a new transfer learning framework, referred to as Transfer Learning via Artificial Neural Network Approximator (TL-ANNA). It builds an Artificial Neural Network (ANN) transfer approximator to transfer the related knowledge from the source task into the target task and reuses the transferred knowledge with a Probabilistic Policy Reuse (PPR) scheme. Specifically, the transfer approximator maps the state of the target task symmetrically to states of the source task with a certain mapping rule, and activates the related knowledge (components of the action-value function) of the source task as the input of the ANNs; it then predicts the quality of the actions in the target task with the ANNs. The target learner uses the PPR scheme to bias the RL with the suggested action from the transfer approximator. In this way, the transfer approximator builds a symmetric knowledge path between the target task and the source task. In addition, two mapping rules for the transfer approximator are designed, namely, Full Mapping Rule and Group Mapping Rule. Experiments performed on the RoboCup soccer Keepaway task verified that the proposed transfer learning methods outperform two other transfer learning methods in both jumpstart and time to threshold metrics and are more robust to the quality of source knowledge. In addition, the TL-ANNA with the group mapping rule exhibits slightly worse performance than the one with the full mapping rule, but with less computation and space cost when appropriate grouping method is used

Static and Discrete Berth Allocation for Large-Scale Marine-Loading Problem by Using Iterative Variable Grouping Genetic Algorithm

In this paper, we study the static discrete berth allocation problems (BAPs) for large-scale time-critical marine-loading scenarios. The objective is to allocate the vessels to different types of berths so that all the vessels can be loaded within the minimum time under the tidal condition. The BAP is formalized as a min–max problem. This problem is rather complex as the vessels and berths are quite numerous in the large-scale marine-loading problem. We analyze this problem from a novel perspective, and find out that this problem has the characteristic of partially separable. Therefore, the iterative variable grouping genetic algorithm (IVGGA) is designed to search the near-optimal berth allocation plans. The vessels and berths are divided into subgroups, and the genetic algorithm (GA) is applied to generate the near-optimal berth allocation plans in each subgroup. To achieve the balance of loading tasks among subgroups, we propose reallocating some vessels among subgroups according to the berth allocation plans in subgroups. To guarantee the convergency of the algorithm, an iterative vessel reallocation policy is devised considering the loading tasks of different types of berths. We demonstrate the proposed algorithm in dealing with large-scale BAPs through numerical experiments. According to the results, we find that the proposed algorithm would have good performance when the number of vessels in each subgroup are kept in medium scale. Compared with the original GA, our algorithm shows the effectiveness of the iterative variable grouping strategy. The performance of our algorithm is almost not changed as the number of vessels and berths increases. The proposed algorithm could obtain efficient berth allocation plans for the large-scale marine-loading problem

Small-Object Detection for UAV-Based Images Using a Distance Metric Method

Object detection is important in unmanned aerial vehicle (UAV) reconnaissance missions. However, since a UAV flies at a high altitude to gain a large reconnaissance view, the captured objects often have small pixel sizes and their categories have high uncertainty. Given the limited computing capability on UAVs, large detectors based on convolutional neural networks (CNNs) have difficulty obtaining real-time detection performance. To address these problems, we designed a small-object detector for UAV-based images in this paper. We modified the backbone of YOLOv4 according to the characteristics of small-object detection. We improved the performance of small-object positioning by modifying the positioning loss function. Using the distance metric method, the proposed detector can classify trained and untrained objects through object features. Furthermore, we designed two data augmentation strategies to enhance the diversity of the training set. We evaluated our method on a collected small-object dataset; the proposed method obtained 61.00% mAP50 on trained objects and 41.00% mAP50 on untrained objects with 77 frames per second (FPS). Flight experiments confirmed the utility of our approach on small UAVs, with satisfying detection performance and real-time inference speed