Kinect-Based Vision System of Mine Rescue Robot for Low Illuminous Environment

This paper presents Kinect-based vision system of mine rescue robot working in illuminous underground environment. The somatosensory system of Kinect is used to realize the hand gesture recognition involving static hand gesture and action. A K-curvature based convex detection method is proposed to fit the hand contour with polygon. In addition, the hand action is completed by using the NiTE library with the framework of hand gesture recognition. In addition, the proposed method is compared with BP neural network and template matching. Furthermore, taking advantage of the information of the depth map, the interface of hand gesture recognition is established for human machine interaction of rescue robot. Experimental results verify the effectiveness of Kinect-based vision system as a feasible and alternative technology for HMI of mine rescue robot

An Improved Case-Based Reasoning Model for Simulating Urban Growth

Developing urban growth models enables a better understanding and planning of sustainable urban areas. Case-based reasoning (CBR), in which historical experience is used to solve problems, can be applied to the simulation of complex dynamic systems. However, when applying CBR to urban growth simulation, problems such as inaccurate case description, a single retrieval method, and the lack of a time control mechanism limit its application accuracy. In order to tackle these barriers, this study proposes a CBR model for simulating urban growth. This model includes three parts: (1) the case expression mode containing the “initial state-geographical feature-result” is proposed to adapt the case expression to the urban growth process; (2) in order to improve the reliability of the results, we propose a strategy to introduce the “retrieval quantity” parameter and retrieve multiple similar cases; and (3) a time factor control method based on demand constraints is proposed to improve the power of time control in the algorithm. Finally, the city of Jixi was used as the study area for simulation, and when the “retrieval quantity” is 10, the simulation accuracy reaches 97.02%, kappa is 85.51, and figure of merit (FoM) is 0.1699. The results showed that the proposed method could accurately analyze urban growth

Sensing Trace-Level Metal Elements in Water Using Chirped Femtosecond Laser Pulses in the Filamentation Regime

Femtosecond filament-induced breakdown spectroscopy (FIBS) is an efficient approach in remote and in situ detection of a variety of trace elements, but it was recently discovered that the FIBS of water is strongly dependent on the large-bandgap semiconductor property of water, making the FIBS signals sensitive to laser ionization mechanisms. Here, we show that the sensitivity of the FIBS technique in monitoring metal elements in water can be efficiently improved by using chirped femtosecond laser pulses, but an asymmetric enhancement of the FIBS intensity is observed for the negatively and positively chirped pulses. We attribute the asymmetric enhancement to their different ionization rates of water, in which the energy of the photons participating in the ionization process in the front part of the negatively chirped pulse is higher than that in the positively chirped pulse. By optimizing the pulse chirp, we show that the limit of detection of the FIBS technique for metal elements in water, e.g., aluminum, can reach to the sub-ppm level, which is about one order of magnitude better than that by the transform-limited pulse. We further examine the FIBS spectra of several representative water samples including commercial mineral water, tap water, and lake water taken from two different environmental zones, i.e., a national park and a downtown business district (Changchun, China), from which remarkably different concentrations of Ca, Na, and K elements of these samples are obtained. Our results provide a possibility of using FIBS for direct and fast metal elemental analysis of water in different field environments

Effects of Water-Fertilizer-Air-Coupling Drip Irrigation on Soil Health Status: Soil Aeration, Enzyme Activities and Microbial Biomass

In order to investigate the effects of water-fertilizer-air-coupling drip irrigation on soil health status, including soil aeration (SA), enzyme activity (EA) and microbial biomass (MB), and its response relationship, this glasshouse experiment was conducted using tomato as the test crop, and we designed two fertilization gradients of 135 and 180 kg N·ha−1, two irrigation levels of 0.6-fold and 1.0-fold of the crop-pan coefficient, and two aeration treatments of 5 and 15 mg·L−1 for the three-factor and two-level completely randomized block experiment. The effects of soil dissolved-oxygen concentration, oxygen diffusion rate, soil respiration rate, soil urease, catalase, phosphatase activities and soil microbial biomass were systematically monitored and analyzed in the middle and at the end of crop growth. A structural equation model was used to comprehensively analyze the response relationship among relevant influencing factors. The results showed that coupled drip irrigation increased the soil’s dissolved oxygen, oxygen diffusion rate and soil respiration rate by 14.05%, 30.14% and 53.74%, respectively. Soil urease, catalase and phosphatase activities increased by 22.83%, 93.01% and 61.35%, respectively. The biomass of bacteria, fungi and actinomycetes increased by 49.06%, 50.18% and 20.39%, respectively. The results of a structural equation model analysis showed that water-fertilizer-air-coupling drip irrigation could effectively improve soil health status, and the descending order of influence was MB > EA > SA. This study provides scientific knowledge to reveal the improvement of soil health status by water-fertilizer-air-coupling drip irrigation

Photo-Promoted Platinum Nanoparticles Decorated MoS₂@Graphene Woven Fabric Catalyst for Efficient Hydrogen Generation

Hydrogen production from water splitting has been considered as an effective and sustainable method to solve future energy related crisis. Molybdenum sulfides (e.g., MoS₂) show promising catalytic ability in hydrogen evolution reaction (HER). Combining MoS₂ with conductive carbon-based materials has aroused tremendous research interest recently. In this work, a highly efficient multiple-catalyst is developed for HER by decorating Pt nanoparticles (Pt NPs) on MoS₂@graphene protected nickel woven fabrics (NiWF) substrate, which comprises the following components: (i) Graphene protected NiWF acts as the underlying substrate, supporting the whole structure; (ii) MoS₂ nanoplates serve as a central and essential photosensitive component, forming a heterostructure with graphene simultaneously; and (iii) on the basis of the intrinsic photoluminescence effect of MoS₂, together with the photoelectric response at the MoS₂/graphene interface, Pt NPs are successfully deposited on the whole structure under illumination. Particularly and foremost, this work emphasizes on discussion and verification of the underlying mechanism for photopromoted electroless Pt NPs deposition. Due to this assembly approach, the usage amount of Pt is controlled at ∼5 wt % (∼0.59 at. %) with respect to the whole catalyst. MoS₂@Substrate with Pt NPs deposited under 643 nm illumination, with the synergistic effect of MoS₂ active sites and Pt NPs, demonstrates the most superior electrocatalytic performance, with negligible overpotential and low Tafel slope of 39.4 mV/dec

Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted Walnut Breaking

This study combined an artificial neural network (ANN) with a genetic algorithm (GA) to obtain the model and optimal process parameters of drying-assisted walnut breaking. Walnuts were dried at different IR temperatures (40 °C, 45 °C, 50 °C, and 55 °C) and air velocities (1, 2, 3, and 4 m/s) to different moisture contents (10%, 15%, 20%, and 25%) by using air-impingement technology. Subsequently, the dried walnuts were broken in different loading directions (sutural, longitudinal, and vertical). The drying time (DT), specific energy consumption (SEC), high kernel rate (HR), whole kernel rate (WR), and shell-breaking rate (SR) were determined as response variables. An ANN optimized by a GA was applied to simulate the influence of IR temperature, air velocity, moisture content, and loading direction on the five response variables, from which the objective functions of DT, SEC, HR, WR, and SR were developed. A GA was applied for the simultaneous maximization of HR, WR, and SR and minimization of DT and SEC to determine the optimized process parameters. The ANN model had a satisfactory prediction ability, with the coefficients of determination of 0.996, 0.998, 0.990, 0.991, and 0.993 for DT, SEC, HR, WR, and SR, respectively. The optimized process parameters were found to be 54.9 °C of IR temperature, 3.66 m/s of air velocity, 10.9% of moisture content, and vertical loading direction. The model combining an ANN and a GA was proven to be an effective method for predicting and optimizing the process parameters of walnut breaking. The predicted values under optimized process parameters fitted the experimental data well, with a low relative error value of 2.51–3.96%. This study can help improve the quality of walnut breaking, processing efficiency, and energy conservation. The ANN modeling and GA multiobjective optimization method developed in this study provide references for the process optimization of walnut and other similar commodities