Design and Stability Analysis of Uncertain Networked Predictive Control Systems with Multiple Forward Channels

This paper is concerned with the design and stability of networked predictive control for uncertain systems with multiple forward channels. The delays and packet dropouts are distributed such that the classic networked predictive control (NPC) needs modifications to be implemented. An improved control signal selection scheme with distributed prediction length is proposed to increase the prediction accuracy and hence achieve better control performance. Moreover, stability analysis results are obtained for both constant and random cases. Interestingly, it is shown that the stability of the closed-loop NPC system is not related to the distributed delays when they are constant and the system model is accurate. Finally, a two-axis milling machine example is given to illustrate the effectiveness of the proposed method

Integrating Second Life into an EFL Program: Students’ Perspectives

Second Life (SL) is a three dimension virtual world imagined and created by its users. To explore various facets of language learning within SL, faculty members of an American university and a Chinese university took an evaluation research approach to search for appropriate ways to integrate SL into an EFL (English as a Foreign Language) program. This paper reports a part of the research efforts with a focus on the Chinese students’ perspectives of an EFL Program in SL. Specifically included in this paper are (a) the Chinese students’ perceived technology readiness to use SL for EFL learning, (b) their perceptions of SL, and (c) the EFL Program implemented in SL. The paper reviews related literature and theoretical support, describes the study’s context and its implementation procedures, and discusses the evaluation results and implications. Finally, the paper shares with the audience some considerations for integrating SL into an EFL progra

Correcting soft errors online in fast fourier transform

While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect soft errors offline in the fast Fourier transform (FFT) after computation finishes, none of the existing ABFT schemes detect soft errors online before the computation finishes. This paper presents an online ABFT scheme for FFT so that soft errors can be detected online and the corrupted computation can be terminated in a much more timely manner. We also extend our scheme to tolerate both arithmetic errors and memory errors, develop strategies to reduce its fault tolerance overhead and improve its numerical stability and fault coverage, and finally incorporate it into the widely used FFTW library - one of the today's fastest FFT software implementations. Experimental results demonstrate that: (1) the proposed online ABFT scheme introduces much lower overhead than the existing offline ABFT schemes; (2) it detects errors in a much more timely manner; and (3) it also has higher numerical stability and better fault coverage

Multi-step prediction of chlorophyll concentration based on Adaptive Graph-Temporal Convolutional Network with Series Decomposition

Chlorophyll concentration can well reflect the nutritional status and algal blooms of water bodies, and is an important indicator for evaluating water quality. The prediction of chlorophyll concentration change trend is of great significance to environmental protection and aquaculture. However, there is a complex and indistinguishable nonlinear relationship between many factors affecting chlorophyll concentration. In order to effectively mine the nonlinear features contained in the data. This paper proposes a time-series decomposition adaptive graph-time convolutional network ( AGTCNSD ) prediction model. Firstly, the original sequence is decomposed into trend component and periodic component by moving average method. Secondly, based on the graph convolutional neural network, the water quality parameter data is modeled, and a parameter embedding matrix is defined. The idea of matrix decomposition is used to assign weight parameters to each node. The adaptive graph convolution learns the relationship between different water quality parameters, updates the state information of each parameter, and improves the learning ability of the update relationship between nodes. Finally, time dependence is captured by time convolution to achieve multi-step prediction of chlorophyll concentration. The validity of the model is verified by the water quality data of the coastal city Beihai. The results show that the prediction effect of this method is better than other methods. It can be used as a scientific resource for environmental management decision-making.Comment: 12 pages, 10 figures, 3 tables, 45 reference

Gradient damage spreading of molten volcanic ash on thermal barrier coatings

Aviation safety and aero engine life are always threatened by dust or ash suspending in the air route which derive from inevitable natural phenomena (volcanic eruption and sand storm) and human productive activity (run way debris, industrial fumes, and coal ash emission). Those floating silicate ash with the low melt temperature (lower than 1100 ºC) will be easily ingested into jet engine and quickly melted due to the fact that the turbine inlet temperature of the current advanced jet engine at cruising altitude (1200-1450 ºC) far exceed the melting point of those silicate ash. Subsequently, these molten ash are deposited on the surface of thermal barrier coatings (TBCs). TBCs is a refractory ceramic layer deposited on the surface of super alloy and can protect these metal at the hot parts (such as combustion chamber, blade and nozzle) from high temperature. However, these silicate deposits will lead to serious spallation and even failure of TBCs. Once the TBCs exfoliate under stress or chemical corrosion because of ash deposition, the engine may stop running during the flight and cause air disaster. Therefore, silicate ash deposition undoubtedly pose a huge obstacle in the development of jet engine. Here, to comprehensively understand the effect of silicate deposits on TBCs, we investigated the subsurface-transverse spreading ring of re-melted volcanic ash (obtained from Tungurahua Volcano, Ecuador, 2014) with various droplet size on the APS TBCs and EB-PVD TBCs respectively at the temperature from 1200 ºC to 1600 ºC over a wide range of duration (Figs. 1a and b). Our results demonstrate that the gradient change of concentration of volcanic ash melt onto TBCs directly leads to the formation of spreading ring in the subsurface-transverse of molten volcanic ash located in the edge of main spreading area (Fig. 1c). These observations imply that the interaction process of molten silicate ash with TBCs is driven not only by vertical infiltration due to gravitation but also by horizontal spreading owing to capillary force. Notably, the infiltration depth of the ring area was deeper than that of the main liquid area, which closely resembles previously observed in ceramic plate (Figs. 1d and e). Overall, we summaries the influence of temperature, holding time and size of droplet on spreading radius and conclude the mechanism of vertical infiltration. Those work is the first step to improving the TBCs and serve as the basic of developing the new generation of aeroengines. Please click Additional Files below to see the full abstract

Research on dedicated rail power supply system for electric cars

in order to improve the endurance capacity and driving safety of electric vehicles, a special track power supply system for electric cars on expressways is studied. The working principle of the main components of the system, such as sliding contact charging track and mechanical charging arm, is simulated and analyzed by using SolidWorks software. The results show that the charging function of the contact track can provide unlimited endurance for electric vehicles, and the guidance function of the track can also ensure the safety of highspeed driving

DifferSketching: How Differently Do People Sketch 3D Objects?

Multiple sketch datasets have been proposed to understand how people draw 3D objects. However, such datasets are often of small scale and cover a small set of objects or categories. In addition, these datasets contain freehand sketches mostly from expert users, making it difficult to compare the drawings by expert and novice users, while such comparisons are critical in informing more effective sketch-based interfaces for either user groups. These observations motivate us to analyze how differently people with and without adequate drawing skills sketch 3D objects. We invited 70 novice users and 38 expert users to sketch 136 3D objects, which were presented as 362 images rendered from multiple views. This leads to a new dataset of 3,620 freehand multi-view sketches, which are registered with their corresponding 3D objects under certain views. Our dataset is an order of magnitude larger than the existing datasets. We analyze the collected data at three levels, i.e., sketch-level, stroke-level, and pixel-level, under both spatial and temporal characteristics, and within and across groups of creators. We found that the drawings by professionals and novices show significant differences at stroke-level, both intrinsically and extrinsically. We demonstrate the usefulness of our dataset in two applications: (i) freehand-style sketch synthesis, and (ii) posing it as a potential benchmark for sketch-based 3D reconstruction. Our dataset and code are available at https://chufengxiao.github.io/DifferSketching/.Comment: SIGGRAPH Asia 2022 (Journal Track

Evolutionary-Based Online Motion Planning Framework for Quadruped Robot Jumping

Offline evolutionary-based methodologies have supplied a successful motion planning framework for the quadrupedal jump. However, the time-consuming computation caused by massive population evolution in offline evolutionary-based jumping framework significantly limits the popularity in the quadrupedal field. This paper presents a time-friendly online motion planning framework based on meta-heuristic Differential evolution (DE), Latin hypercube sampling, and Configuration space (DLC). The DLC framework establishes a multidimensional optimization problem leveraging centroidal dynamics to determine the ideal trajectory of the center of mass (CoM) and ground reaction forces (GRFs). The configuration space is introduced to the evolutionary optimization in order to condense the searching region. Latin hypercube sampling offers more uniform initial populations of DE under limited sampling points, accelerating away from a local minimum. This research also constructs a collection of pre-motion trajectories as a warm start when the objective state is in the neighborhood of the pre-motion state to drastically reduce the solving time. The proposed methodology is successfully validated via real robot experiments for online jumping trajectory optimization with different jumping motions (e.g., ordinary jumping, flipping, and spinning).Comment: IROS202