A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction

This paper presents an engine able to forecast jointly the concentrations of the main pollutants harming people's health: nitrogen dioxyde (NO2), ozone (O3) and particulate matter (PM2.5 and PM10, which are respectively the particles whose diameters are below 2.5 um and 10 um respectively). 
The forecasts are performed on a regular grid (the results presented in the paper are produced with a 0.5° resolution grid over Europe and the United States) with a neural network whose architecture includes convolutional LSTM blocks. The engine is fed with the most recent air quality monitoring stations measures available, weather forecasts as well as air quality physical and chemical model (AQPCM) outputs. The engine can be used to produce air quality forecasts with long time horizons, and the experiments presented in this paper show that the 4 days forecasts beat very significantly simple benchmarks. 
A valuable advantage of the engine is that it does not need much computing power: the forecasts can be built in a few minutes on a standard GPU. Thus, they can be updated very frequently, as soon as new air quality measures are available (generally every hour), which is not the case of AQPCMs traditionally used for air quality forecasting. 
The engine described in this paper relies on the same principles as a prediction engine deployed and used by Plume Labs in several products aiming at providing air quality data to individuals and businesses.

PlumeNet: Large-Scale Air Quality Forecasting Using A Convolutional LSTM Network

FDN-learning: Urban PM2.5-concentration Spatial Correlation Prediction Model Based on Fusion Deep Neural Network

Machine Learning based Models for Fresh Produce Yield and Price Forecasting for Strawberry Fruit

A new positional accuracy reliability analysis method of industrial robots is proposed based on the statistical moment similarity of positional error. The first-two order statistical moments of positional error at some positions are accurately obtained through the differential kinematics method to reduce the computational cost of the proposed method. In practical engineering, the statistical moments of positional error can also be obtained from the limited experimental data. Then, the statistical moment similarity of positional error is quantified by the semivariance function. Besides, the first-two order moments of positional error at any position can be calculated according to the statistical moment similarity. Thus, the positional accuracy reliability analysis of any position can be realized by the first-two order moments according to the saddlepoint approximation method. Besides, an optimal position with the lowest failure probability of positional error can be searched based on the statistical moment similarity. The effectiveness of the proposed method is manifest through calculating the positional accuracy reliability and the optimal position with the lowest failure probability of a 6-degree-of-freedom (6-DoF) industrial robot in a cubic space. 

Reliability analysis of industrial robot positional errors based on statistical moment similarity metrics

Environment protection department need to grasp the concentration of PM2.5 in a future moment when monitoring. However, the existing PM2.5 prediction studies only forecast short-term time points, and cannot accurately give the trend of the next period of time. In this paper, a PM2.5 prediction model based on Att-ConvLSTM model integrated training method is established by the advantage of ConvLSTM to obtain spatiotemporal information. Then, Experiments were performed using DNN, ARIMA and LSTM as control model with Att-ConvLSTM model and used it for application test. The result demonstrated that prediction model can extract spatiotemporal features with attention mechanism and ConvLSTM. The model can reduce the generalization error of the model when predicting other observation points.

Att-ConvLSTM: PM2.5 Prediction Model and Application.

Assistive navigation system for visually impaired persons (VIPs) mainly focuses on wayfinding and obstacle detection. Previous work on running navigation for VIPs is limited with less users' perspective. We created the running guide, a pair of wearable glasses based on the insights from user research, enabling visually impaired (VI) runners to navigate the running route and finish the marathon more independently and confidently. Our system provides two types of feedback: (1) spatial 3D auditory feedback from different sources telling the VI runners the right direction; (2) vibrotactile feedback that simulates vibrations when runners run to the boundary of the virtual running track we set before the marathon. We discuss our interaction design and hardware implementation, as well as the social meaning to the visually impaired persons.

Running guide: design of a marathon navigation system for visually impaired people

Novel reliability evaluation method combining active learning kriging and adaptive weighted importance sampling

In order to obtain stable and high-precision motion tracking in mixed reality (MR) simulation of the complex system, an optical-inertial hybrid tracking method is proposed, which combines the accuracy of optical motion capture equipment and the robustness of inertial motion capture equipment, and can reduce the occlusion problem of entity scene on the premise of ensuring the localization accuracy. The method has been applied to the ergonomic evaluation of large civil aircraft development processes, and the effectiveness and usability of the method have been verified.

A human hybrid tracking and localization method for mixed reality simulation of complex system

In order to analyze the proposed method, this paper takes the cockpit as the complex system, since the traditional cockpit design evaluation method can set up an actual engineering prototype. Although this method can realize the accurate evaluation of the internal structure layout, control devices, and interface display of the cockpit, it is difficult to make modifications once the production is completed, which is not suitable for multi-scheme comparison and rapid iteration in the early stage of civil aircraft design. In this regard, this paper establishes the civil aircraft cockpit physical environment and the corresponding virtual cockpit environment based on the virtual-real matching technology, then proposes a mixed reality simulation evaluation method so as to realize the real-time ergonomic evaluation of the digital human body followed by a real person. Experimental results show that this mixed reality simulation evaluation method can effectively improve the evaluation accuracy of cockpit design, ensure the efficiency of multi-scheme comparison and optimization iteration in the early stage of cockpit design.

A mixed reality simulation evaluation method for complex system

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