Adaptive Monte Carlo localization (AMCL) algorithm has a limited pose accuracy because of the nonconvexity of the laser sensor model, the complex and unstructured features of the working environment, the randomness of particle sampling, and the final pose selection problem. In this paper, an improved AMCL algorithm is proposed, aiming to build a laser radar-based robot localization system in a complex and unstructured environment, with a LIDAR point cloud scan-matching process after the particle score calculating process. The weighted mean pose of AMCL particle swarm is used as the initial pose of the scan matching process. The LIDAR point cloud is matched with the probability grid map from coarse to fine using the Gaussian-Newton method, which results in more accurate poses. Moreover, the scan-matching pose is added into the particle swarm as a high-weight particle. So the particle swarm after resampling will be more concentrated in the correct position. The particle filter and the scan-matching process form a closed loop, thus enhancing the localization accuracy of mobile robots. The experiment results demonstrate that the proposed improved AMCL algorithm is superior to the traditional AMCL algorithm in the complex and unstructured environment, by exploiting the high-accuracy characteristic of scan matching while inheriting the stability of AMCL.
Background The presence of residual DNA carried by biological products in the body may lead to an increased oncogenicity, infectivity, and immunomodulatory risk. Therefore, current agencies including WHO, EU, and the FDA limited the accepted amounts of residual DNA (less than 10 ng or 100 pg/dose). Among the methods of detecting residual DNA, qPCR is considered to be the most practical for residual DNA quantitation due to its sensitivity, accuracy, precision, and time-saving. Results In this study, the detection capacity of this method was determined by comparing the detected concentration of the commercial kit and the self-designed primer/probe set after the same treatment of the extraction method. Then, a universal sample pretreatment method based on a co-precipitant was optimized. The validation results demonstrated that the method has appropriate specificity, sensitivity, accuracy, and precision according to ICH guidelines. The limit of detection and quantitation reached 3 fg/ul and 0.3 pg/reaction respectively, which satisfies the requirement of limit of residual DNA detection in biologics. Spike recovery (82.3–105.7%) showed that the proposed qPCR assay was accurate and has good extraction efficiency. Moreover, the precision of the method based on intra- and inter-assay was 0.065–0.452% and 0.471–1.312%, respectively. Conclusions These results all indicated that the method for determination of residual DNA in biological products expressed from CHO cells is sensitive, accurate and robust. Electronic supplementary material The online version of this article (10.1186/s12575-019-0105-1) contains supplementary material, which is available to authorized users.
Recent years have seen human robot collaboration (HRC) quickly emerged as a hot research area at the intersection of control, robotics, and psychology. While most of the existing work in HRC focused on either low-level human-aware motion planning or HRC interface design, we are particularly interested in a formal design of HRC with respect to high-level complex missions, where it is of critical importance to obtain an accurate and meanwhile tractable human model. Instead of assuming the human model is given, we ask whether it is reasonable to learn human models from observed perception data, such as the gesture, eye movements, head motions of the human in concern. As our initial step, we adopt a partially observable Markov decision process (POMDP) model in this work as mounting evidences have suggested Markovian properties of human behaviors from psychology studies. In addition, POMDP provides a general modeling framework for sequential decision making where states are hidden and actions have stochastic outcomes. Distinct from the majority of POMDP model learning literature, we do not assume that the state, the transition structure or the bound of the number of states in POMDP model is given. Instead, we use a Bayesian non-parametric learning approach to decide the potential human states from data. Then we adopt an approach inspired by probably approximately correct (PAC) learning to obtain not only an estimation of the transition probability but also a confidence interval associated to the estimation. Then, the performance of applying the control policy derived from the estimated model is guaranteed to be sufficiently close to the true model. Finally, data collected from a driver-assistance test-bed are used to train the model, which illustrates the effectiveness of the proposed learning method.
There has been an increasing interest of integrating blockchain into cyber-physical systems (CPS). The design of password hashing schemes (PHSs) is in the core of blockchain security. However, no existing PHS seems to meet both the requirements of sufficient security and small code size for blockchain-based CPSs. In this article, a novel memory-hard PHS based on the classic PBKDF2 is proposed. Evaluation results show that the proposed scheme is promising for blockchain-based CPS, as it manages to provide enhanced security in comparison to PBKDF2 with limited increase in code size.
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