Tactical Trajectory Planning for Stealth Unmanned Aerial Vehicle to Win the Radar Game

In this paper, problem of planning tactical trajectory for stealth unmanned aerial vehicle (UAV) to win the radar game is studied. Three principles of how to win the radar game are presented, and their utilizations for stealth UAV to evade radar tracking are analysed. The problem is formulated by integrating the model of stealth UAV, the constraints of radar detecting and the multi-objectives of the game. The pseudospectral multi-phase optimal control based trajectory planning algorithm is developed to solve the formulated problem. Pseudospectral method is employed to seek the optimal solution with satisfying convergence speed. The results of experiments show that the proposed method is feasible and effective. By following the planned trajectory with several times of switches between exposure and stealth, stealth UAV could win the radar game triumphantly.Defence Science Journal, 2012, 62(6), pp.375-381, DOI:http://dx.doi.org/10.14429/dsj.62.268

A polynomial time optimal algorithm for robot-human search under uncertainty

This paper studies a search problem involving a robot that is searching for a certain item in an uncertain environment (e.g., searching minerals on Moon) that allows only limited interaction with humans. The uncertainty of the environment comes from the rewards of undiscovered items and the availability of costly human help. The goal of the robot is to maximize the reward of the items found while minimising the search costs. We show that this search problem is polynomially solvable with a novel integration of the human help, which has not been studied in the literature before. Furthermore, we empirically evaluate our solution with simulations and show that it significantly outperforms several benchmark approaches

An Integrated Multicriterion hp

Pseudospectral methods (PMs) for solving general optimal control problems (OCPs) attract an increasing amount of research and application in engineering. It is challenging to improve the convergence rate, the solution accuracy, and the applicability of PMs, especially for nonsmooth problems. Existing hp-adaptive PMs consider only one heuristic criterion, which cannot produce satisfactory performance in many cases. In this paper, we propose a novel method which integrates multicriterion to hp-adaptive PM, in order to further improve the performance. For this purpose, we first devise an OCP solving framework of hp-adaptive PM. We then design a multicriterion hp-adaptive strategy which introduces prior knowledge, intermediate error and curvature as useful criterions for adaptive refinement. We last present an iterative procedure for solving general nonlinear OCPs. Results from two examples show that our method significantly outperforms competitors on the convergence rate and the solution accuracy. The method is practical and effective for direct solving of various OCPs in a broad range of engineering

Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection

Monocular 3D lane detection is a challenging task due to its lack of depth information. A popular solution is to first transform the front-viewed (FV) images or features into the bird-eye-view (BEV) space with inverse perspective mapping (IPM) and detect lanes from BEV features. However, the reliance of IPM on flat ground assumption and loss of context information make it inaccurate to restore 3D information from BEV representations. An attempt has been made to get rid of BEV and predict 3D lanes from FV representations directly, while it still underperforms other BEV-based methods given its lack of structured representation for 3D lanes. In this paper, we define 3D lane anchors in the 3D space and propose a BEV-free method named Anchor3DLane to predict 3D lanes directly from FV representations. 3D lane anchors are projected to the FV features to extract their features which contain both good structural and context information to make accurate predictions. In addition, we also develop a global optimization method that makes use of the equal-width property between lanes to reduce the lateral error of predictions. Extensive experiments on three popular 3D lane detection benchmarks show that our Anchor3DLane outperforms previous BEV-based methods and achieves state-of-the-art performances. The code is available at: https://github.com/tusen-ai/Anchor3DLane.Comment: Accepted by CVPR 202

C1-C2 alkyl aminiums in urban aerosols: Insights from ambient and fuel combustion emission measurements in the Yangtze River Delta region of China

We measured low molar-mass alkyl aminiums (methylaminium, dimethylaminium, ethylaminium and diethylaminium) in urban aerosols in the Yangtze River Delta region of eastern China in August 2014 and from November 2015 to May 2016. After examining artifact formation on sample filters, methylaminium, dimethylaminium and ethylaminium concentrations were quantified. The three C1-C2 aminiums exhibited a unimodal size distribution that maximized between 0.56 and 1.0 μm. Their concentrations in PM2.5 were 5.7 ± 3.2 ng m−3, 7.9 ± 5.4 ng m−3 and 20.3 ± 16.6 ng m−3, respectively, with higher concentrations during the daytime and in warm seasons. On new particle growth days, amine uptake to particles larger than 56 nm was barely enhanced. The molar ratios of individual aminium/NH4+ in PM2.5 were on the order of 10−4 and 10−3. Aminiums were thus far less to out-compete ammonium (NH4+) in neutralizing acidic species in particle sizes down to 56 nm. Abundant nitrate (NO3−/SO42− molar ratio = ∼3) and its correlation to methylaminium and ethylaminium implied that nitrate might be more important aminium salt than sulfate in urban aerosols of this area. Direct measurement of particle-phase amine emission from coal and biomass burning showed that coal burning is an important atmospheric amine source, considering coal burning is top-ranked particulate matter source in China

Multi-agent patrolling under uncertainty and threats

We investigate a multi-agent patrolling problem in large stochasticenvironments where information is distributed alongside threats. The informationand threat at each location are respectively modelled as a multi-state Markovchain, whose states are not observed until the location is visited by an agent.While agents obtain information at a location, they may suffer attacks from thethreat at that location. The goal for the agents is to gather as much informationas possible while mitigating the damage incurred. We formulate this problem asa Partially Observable Markov Decision Process (POMDP) and propose a computationallyefficient algorithm to solve it.We empirically evaluate our algorithmin a simulated environment, and show that it outperforms a greedy algorithm upto 43% for 10 agents in a large graph

Coal-rock recognition method based on distance metric learning

A coal-rock recognition method based on distance metric learning was proposed. In the method, features of coal-rock images are extracted firstly from training sets of coal-rock images. Then a fit distance metric is gotten, which can make distance between any two features of coal samples or the ones of rock samples smaller and distance between features of coal samples and rock samples bigger, so as to improve classification and recognition effect. Finally, classifier is used to recognize coal-rock. The experimental results show when extracted coal-rock features are LBP, HOG or GLCM features, the method has higher coal-rock recognition rate than coal-rock recognition methods based on Euclidean distance, LDA or ITML

Multi-Vortex Regulation for Efficient Fluid and Particle Manipulation in Ultra-Low Aspect Ratio Curved Microchannels

Inertial microfluidics enables fluid and particle manipulation for biomedical and clinical applications. Herein, we developed a simple semicircular microchannel with an ultra-low aspect ratio to interrogate the unique formations of the helical vortex and Dean vortex by introducing order micro-obstacles. The purposeful and powerful regulation of dimensional confinement in the microchannel achieved significantly improved fluid mixing effects and fluid and particle manipulation in a high-throughput, highly efficient and easy-to-use way. Together, the results offer insights into the geometry-induced multi-vortex mechanism, which may contribute to simple, passive, continuous operations for biochemical and clinical applications, such as the detection and isolation of circulating tumor cells for cancer diagnostics

ESOP and Corporate Sustainable Growth

The Employee Stock Ownership Plan (ESOP) is a long-term corporate welfare policy that allows employees to share the profits and growth benefits of the enterprise by owning the ordinary share of the enterprise. It has always been a research hotspot at home and abroad. Sustainable growth refers to the healthy and sustained growth of enterprises. ESOP has been re-implemented in China since 2014. Using dual fixed effects model, this paper empirically analyzes 6940 observations of Chinese listed companies from 2014 to 2018. We study whether ESOP can improve the sustainable growth rate of enterprises by allowing employees to hold equity, linking their personal interests to the interests of enterprises, and thus playing an incentive and supervisory role to effectively reduce enterprise agency costs. In the research process, we find that the data can help us objectively analyze the economic management problems of enterprises. However, when using the data for analysis, the correlation coefficient and significance should be analyzed together