Modeling simulation and parameters optimization for hydraulic impactor

The paper analyzes the working principle of hydraulic impactor, describes the return and stroke order of action and establishes the nonlinear mathematical model describing its dynamic characteristic. The simulation model of hydraulic impactor is established based on AMESim. The structural features of trial machine is used to constrain variables, the piston speed is assigned as the optimizing objective, and the NLPSL algorithm is used to optimize the parameters of system model of hydraulic impactor, after which the system performance is obviously improved

Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images.

Although extreme learning machines (ELM) have been successfully applied for the classification of hyperspectral images (HSIs), they still suffer from three main drawbacks. These include: 1) ineffective feature extraction (FE) in HSIs due to a single hidden layer neuron network used; 2) ill-posed problems caused by the random input weights and biases; and 3) lack of spatial information for HSIs classification. To tackle the first problem, we construct a multilayer ELM for effective FE from HSIs. The sparse representation is adopted with the multilayer ELM to tackle the ill-posed problem of ELM, which can be solved by the alternative direction method of multipliers. This has resulted in the proposed multilayer sparse ELM (MSELM) model. Considering that the neighboring pixels are more likely from the same class, a local block extension is introduced for MSELM to extract the local spatial information, leading to the local block MSELM (LBMSELM). The loopy belief propagation is also applied to the proposed MSELM and LBMSELM approaches to further utilize the rich spectral and spatial information for improving the classification. Experimental results show that the proposed methods have outperformed the ELM and other state-of-the-art approaches

A pupil-positioning method based on the starburst model

Human eye detection has become an area of interest in the field of computer vision with an extensive range of applications in human-computer interaction, disease diagnosis, and psychological and physiological studies. Gaze-tracking systems are an important research topic in the human-computer interaction field. As one of the core modules of the head-mounted gaze-tracking system, pupil positioning affects the accuracy and stability of the system. By tracking eye movements to better locate the center of the pupil, this paper proposes a method for pupil positioning based on the starburst model. The method uses vertical and horizontal coordinate integral projections in the rectangular region of the human eye for accurate positioning and applies a linear interpolation method that is based on a circular model to the reflections in the human eye. In this paper, we propose a method for detecting the feature points of the pupil edge based on the starburst model, which clusters feature points and uses the RANdom SAmple Consensus (RANSAC) algorithm to perform ellipse fitting of the pupil edge to accurately locate the pupil center. Our experimental results show that the algorithm has higher precision, higher efficiency and more robustness than other algorithms and excellent accuracy even when the image of the pupil is incomplete.Science and Technology Support Plan Project of Hebei Province (grant numbers 17210803D and 19273703D Science and Technology Spark Project of the Hebei Seismological Bureau (grant number DZ20180402056) Education Department of Hebei Province (grant number QN2018095) Polytechnic College of Hebei University of Science and Technolog

Detecting movements of a target using face tracking in wireless sensor networks

Abstract—Target tracking is one of the key applications of wireless sensor networks (WSNs). Existing work mostly requires organizing groups of sensor nodes with measurements of a target’s movements or accurate distance measurements from the nodes to the target, and predicting those movements. These are, however, often difficult to accurately achieve in practice, especially in the case of unpredictable environments, sensor faults, etc. In this paper, we propose a new tracking framework, called FaceTrack, which employs the nodes of a spatial region surrounding a target, called a face. Instead of predicting the target location separately in a face, we estimate the target’s moving toward another face. We introduce an edge detection algorithm to generate each face further in such a way that the nodes can prepare ahead of the target’s moving, which greatly helps tracking the target in a timely fashion and recovering from special cases, e.g., sensor fault, loss of tracking. Also, we develop an optimal selection algorithm to select which sensors of faces to query and to forward the tracking data. Simulation results, compared with existing work, show that FaceTrack achieves better tracking accuracy and energy efficiency. We also validate its effectiveness via a proof-of-concept system of the Imote2 sensor platform. Index Terms—Wireless sensor networks, target tracking, sensor selection, edge detection, face tracking, fault tolerance Ç

RGB: a scalable and reliable group membership protocol in mobile Internet

We propose a membership protocol for group commu-nications in mobile Internet. The protocol is called RGB, which is the acronym of “a Ring-based hierarchy of ac-cess proxies, access Gateways, and Border routers”. RGB runs in a parallel and distributed way in the sense that each network entity in the ring-based hierarchy maintains local information about its possible leader, previous, next, par-ent and child neighbors, and that each network entity inde-pendently collects/generates membership change informa-tion, which is propagated by the one-round membership al-gorithm concurrently running in all the logical rings. We prove that the proposed protocol is scalable in the sense that the scalability of a ring-based hierarchy is as good as that of a tree-based hierarchy. We also prove that the proposed protocol is reliable, in the sense that, with high probability of 99.500%, a ring-based hierarchy with up to 1000 access proxies attached by a large number of mobile hosts will not partition when node faulty probability is bounded by 0.1%; if at most 3 partitions are allowed, then the Function-Well probability of the hierarchy is 99.999 % accordingly. 1

MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning

Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine-tuning of low-level black-box optimizers. However, this field is hindered by the lack of a unified benchmark. To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods. MetaBox offers a flexible algorithmic template that allows users to effortlessly implement their unique designs within the platform. Moreover, it provides a broad spectrum of over 300 problem instances, collected from synthetic to realistic scenarios, and an extensive library of 19 baseline methods, including both traditional black-box optimizers and recent MetaBBO-RL methods. Besides, MetaBox introduces three standardized performance metrics, enabling a more thorough assessment of the methods. In a bid to illustrate the utility of MetaBox for facilitating rigorous evaluation and in-depth analysis, we carry out a wide-ranging benchmarking study on existing MetaBBO-RL methods. Our MetaBox is open-source and accessible at: https://github.com/GMC-DRL/MetaBox.Comment: Accepted at NuerIPS 202

Screening of functional antidotes of RNA aptamers against bovine thrombin

AbstractA specific RNA aptamer (T705) against bovine thrombin had been obtained after seven rounds of SELEX (systematic evolution of ligands by exponential enrichment) selection from a random RNA library previously. In order to further investigate the relationship between the structure and function of this aptamer, three truncated RNA aptamers, T705a, T705b and T705c, were designed according to the secondary structure of T705 RNA. Our results showed that T705c keeping the precise stem–loop structure but lacking most of the stem region sequence of T705 could inhibit clot formation in vitro in the same way as its parental form. We also report here that single-stranded DNA (ssDNA) antisense oligonucleotides, c′ and c′-22, which were complementary to different portions of T705c could act as efficient antidotes reversing the inhibitory activity of T705. It is demonstrated for the first time that ssDNA antisense oligonucleotides are potential antidotes of RNA aptamers and this may be an effective, rapid strategy to find antidotes of RNA aptamers which would be of important usefulness in basic research and drug screening

Modeling simulation and parameters optimization for hydraulic impactor

The paper analyzes the working principle of hydraulic impactor, describes the return and stroke order of action and establishes the nonlinear mathematical model describing its dynamic characteristic. The simulation model of hydraulic impactor is established based on AMESim. The structural features of trial machine is used to constrain variables, the piston speed is assigned as the optimizing objective, and the NLPSL algorithm is used to optimize the parameters of system model of hydraulic impactor, after which the system performance is obviously improved