Fast Optimal Joint Tracking-Registration for Multi-Sensor Systems

Sensor fusion of multiple sources plays an important role in vehicular systems to achieve refined target position and velocity estimates. In this article, we address the general registration problem, which is a key module for a fusion system to accurately correct systematic errors of sensors. A fast maximum a posteriori (FMAP) algorithm for joint registration-tracking (JRT) is presented. The algorithm uses a recursive two-step optimization that involves orthogonal factorization to ensure numerically stability. Statistical efficiency analysis based on Cram\`{e}r-Rao lower bound theory is presented to show asymptotical optimality of FMAP. Also, Givens rotation is used to derive a fast implementation with complexity O(n) with $n$ the number of tracked targets. Simulations and experiments are presented to demonstrate the promise and effectiveness of FMAP

Deep reinforcement learning for real-time economic energy management of microgrid system considering uncertainties

The electric power grid is changing from a traditional power system to a modern, smart, and integrated power system. Microgrids (MGs) play a vital role in combining distributed renewable energy resources (RESs) with traditional electric power systems. Intermittency, randomness, and volatility constitute the disadvantages of distributed RESs. MGs with high penetrations of renewable energy and random load demand cannot ignore these uncertainties, making it difficult to operate them effectively and economically. To realize the optimal scheduling of MGs, a real-time economic energy management strategy based on deep reinforcement learning (DRL) is proposed in this paper. Different from traditional model-based approaches, this strategy is learning based, and it has no requirements for an explicit model of uncertainty. Taking into account the uncertainties in RESs, load demand, and electricity prices, we formulate a Markov decision process for the real-time economic energy management problem of MGs. The objective is to minimize the daily operating cost of the system by scheduling controllable distributed generators and energy storage systems. In this paper, a deep deterministic policy gradient (DDPG) is introduced as a method for resolving the Markov decision process. The DDPG is a novel policy-based DRL approach with continuous state and action spaces. The DDPG is trained to learn the characteristics of uncertainties of the load, RES output, and electricity price using historical data from real power systems. The effectiveness of the proposed approach is validated through the designed simulation experiments. In the second experiment of our designed simulation, the proposed DRL method is compared to DQN, SAC, PPO, and MPC methods, and it is able to reduce the operating costs by 29.59%, 17.39%, 6.36%, and 9.55% on the June test set and 30.96%, 18.34%, 5.73%, and 10.16% on the November test set, respectively. The numerical results validate the practical value of the proposed DRL algorithm in addressing economic operation issues in MGs, as it demonstrates the algorithm’s ability to effectively leverage the energy storage system to reduce the operating costs across a range of scenarios

Online-Learned Classifiers for Robust Multitarget Tracking

Abstract — In this paper, we propose online-learned classifiers for data association in multitarget tracking. The classifiers are dynamically constructed and incrementally online learned using image patches, which are associated based on location proimity. A biological inspired architecture is used to compute the classification label of image patch. The extracted image patches are coded and learned by a 3-layer neural network that implements in-place learning. We employ minimum-cost network flow optimization to associate tracks with the image patches based on their appearance and location proximities. The presented framework is applied to learn 11 targets encountered in a PETS2009 data set. Cross validation results show that the overall recognition accuracy is above 93%. The comparison with other learning algorithms is promising. The results of the implemented multitarget tracker demonstrate the effectiveness of the approach. Key Workds: Intelligent video surveillance system, object learning, and biologically inspired neural network

MiR-30a-5p hampers proliferation of lung squamous cell carcinoma through targeting FBXO45

Objective. Studies have elaborated the inhibition of miR-30a-5p on the proliferation of cancer cells. However, the regulatory mechanism of how miR30a-5p works in lung squamous cell carcinoma (LUSC) cells is obscure. Methods. Data of miRNAs/mRNAs in LUSC tissue (The Cancer Genome Atlas (TCGA)) were accessed. A differential upstream miRNA (miR-30a-5p) was obtained by differential analysis. Downstream target mRNAs were predicted and screened by several databases. The function pathways of target protein in cells were determined by gene set enrichment analysis (GSEA). Abnormal expression levels of FBXO45 and miR-30a-5p were evaluated in three LUSC cell lines. The expression levels of FBXO45 mRNA and miR-30a5p were analyzed by qRT-PCR. Western blot method was employed to assess protein levels of FBXO45, Cyclin E1, Cdk4 and Cyclin D1. How the two researched genes interact was testified by dual-luciferase method. Cell proliferative ability was compared by CCK-8 and colony formation methods. Moreover, cell cycle was tested by flow cytometry. Results. MiR-30a-5p was tested to be noticeably down-regulated in LUSC cell lines. Up-regulated FBXO45 in LUSC was targeted by miR-30a-5p. Overexpressing miR-30a-5p modulated proliferation and cell cycle in LUSC via inhibiting FBXO45. Conclusion. MiR-30a-5p hindered FBXO45 expression to repress the proliferation of LUSC. FBXO45/miR-30a-5p may shed light on future molecular treatment of LUSC

Motor initiated expectation through topdown connections as abstract context in a physical world

Abstract—Recently, it has been shown that top-down connections improve recognition in supervised learning. In the work presented here, we show how top-down connections represent temporal context as expectation and how such expectation assists perception in a continuously changing physical world, with which an agent interacts during its developmental learning. In experiments in object recognition and vehicle recognition using two types of networks (which derive either global or local features), it is shown how expectation greatly improves performance, to nearly 100 % after the transition periods. We also analyze why expectation will improve performance in such real world contexts. I