A fast procedure for the estimation of the hydrogen storage capacity by cryoadsorption of metal-organic framework materials from their available porous properties

In order to identify the best porous materials for the cryogenic physisorption of hydrogen, high-throughput calculations are performed starting, i.e., from the collected information in crystallographic databases. However, these calculations, like molecular simulations, require specific training and significant computational cost. Herein, a relatively simple procedure is proposed to estimate and compare hydrogen uptakes at 77 K and pressure values from 40 bar starting from the porous properties of MOF materials, without involving simulation tools. This procedure uses definitions for adsorption and considers the adsorbed phase as an incompressible fluid whose pressure-density change is that for the liquid phase at 19 K. For the 7000 structures from the CoRE MOF database, the average error of the predictions is only of 1% from reference values at 100 bar, with an SD of ±8%. This accuracy is lower than that from simulation tools, but involving lower computational cost and training.     

Design and analysis of a new wave energy converter based on a point absorber and a hydraulic system harvesting energy from waves near the shore in calm seas

The aim of this paper is to illustrate the design of a new wave energy converter, composed of a point absorber and a hydraulic system (power take off) and sized for recovering energy in calm seas from waves near the shore. The point absorber is consisting of a rectangular shaped buoy integrating a piston pump. The set buoy‐pump oscillates under the waves action and moves natural water in a closed circuit hydraulic system (power take off) composed of a piping connecting the piston pump itself, a pressurized reservoir, a hydraulic turbine and a discharge tank. The methodology adopted for designing the main constituents involves a 1D mathematical model, settled for understanding the motion of the buoy under the hypothesis of regular waves and fully developed sea, and a sizing procedure applied for the design of all the components of the hydraulic system. The project related to the Calabrian site of Cetraro (Mediterranean Sea—south Italy) led to designing a system with a 4 m large buoy, associated with a small 13 cm diameter micro Pelton turbine, so that more than 22 000 kWh could be recovered in a year.     

Review of lithium-ion battery state of charge estimation

The technology deployed for lithium-ion battery state of charge (SOC) estimation is an important part of the design of electric vehicle battery management systems. Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries, thereby improving discharge efficiency and extending cycle life. In this study, the key lithium-ion battery SOC estimation technologies are summarized. First, the research status of lithium-ion battery modeling is introduced. Second, the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed. Third, the development status and advantages and disadvantages of SOC estimation methods are summarized. Finally, the current research problems and prospects for development trends are summarized.     

Electric vehicle energy consumption modelling and estimation—A case study

Electric vehicles (EVs) have a limited driving range compared to conventional vehicles. Accurate estimation of EV's range is therefore a significant need to eliminate “range anxiety” that refers to drivers' fear of running out of energy while driving. However, the range estimators used in the currently available EVs are not sufficiently accurate. To overcome this issue, more accurate range estimation techniques are investigated. Nonetheless, an accurate power‐based EV energy consumption model is crucial to obtain a precise range estimation. This paper describes a study on EV energy consumption modelling. For this purpose, EV modelling is carried out using MATLAB/Simulink software based on a real EV in the market, the BMW i3. The EV model includes vehicle powertrain system and longitudinal vehicle dynamics. The powertrain is modelled using efficiency maps of the electric motor and the power electronics' data available for BMW i3. It also includes a transmission and a battery model (ie, Thevenin equivalent circuit model). A driver model is developed as well to control the vehicle's speed and to represent human driver's behaviour. In addition, a regenerative braking strategy, based on a series brake system, is developed to model the behaviour of a real braking controller. Auxiliary devices are also included in the EV model to improve energy consumption estimation accuracy as they can have a significant impact on that. The vehicle model is validated against published energy consumption values that demonstrates a satisfactory level of accuracy with 2% to 6% error between simulation and experimental results for Environmental Protection Agency and NEDC tests.     

Parameter identification for Wiener-finite impulse response system with output data of missing completely at random mechanism and time delay

In this paper, the parameter estimation issue of Wiener system with random time delay and missing output data is studied. The linear part of Wiener system is described by Finite Impulse Response (FIR) model. The mathematical formula of the Expectation Maximum algorithm to identify Wiener-FIR system that contains the random time delay and the nonlinear output data in missing completely at random mechanism is derived, which is never considered before. To obtain the unmeasurable intermediate variable in Wiener-FIR system, the idea of auxiliary model is adopted. The time delay and system parameters can be estimated simultaneously by this method. Numerical example and the identification of water tank system example are carried out, the effectiveness of the algorithm is proved.     

Patterns and stability of food preferences among a national representative sample of young,middle-aged,and elderly adults in China: A latent transition analysis

The purpose of this study was to explore the patterns and stability of, as well as the predictors for, Chinese adults’ food preferences. With the panel data set derived from the China Health and Nutrition Survey (CHNS), latent class analysis (LCA) and latent transition analysis (LTA) were employed to analyze the data of 8850 adults aged 18–40 years (young), 41–60 years (middle-aged) and 61 years above (elderly) over a 4-year period (year 2011 as baseline and year 2015 as follow-up). Latent class analysis revealed three types of food preferences: preference for fruits and vegetables, preference for a varied diet and low food preference. Gender, dietary knowledge, residence, education, and BMI were revealed as significant predictors of the class memberships. From baseline to follow-up, the most stable status in young adult group was preference for fruits and vegetables as it had a high transition probability 0.764 of remaining in the same class. While low food preference and preference for a varied diet statuses were unstable, they had transition probabilities of 0.590 and 0.554, respectively, moving to the preference for fruits and vegetables in year 2015. For middle-aged and elderly groups, the most stable and unstable statuses were preference for fruits and vegetables and preference for a varied diet, respectively. The unstable classes all had over 50% probability of moving to the preference for fruits and vegetables after four years. Potential explanations and implications are also discussed.     

Application of large scale PIV in river surface turbulence measurements and water depth estimation

Large-Scale Particle Image Velocimetry (LSPIV) has emerged as a reliable technology to measure river surface flow velocity distribution and can be applied to estimate river discharge. Fewer studies have explored the capability of surface turbulence measurements using LSPIV. In this paper, LSPIV is applied to evaluate statistics of surface turbulence of a natural river. Turbulence measurements including velocity fluctuation, velocity spectra and the dissipation rate of turbulent kinetic energy (TKE) are validated by comparing with those measured by an Acoustic Doppler Velocimeter (ADV). Traditionally, estimation of stream discharge through LSPIV needs a secondary measurement to determine river bathymetry and water depth. A new method is presented here to demonstrate that for a fully developed and channel-controlled flow, the cross section geometry can be estimated from the combined measurements of surface mean velocity and the dissipation rate, following the Manning-Strickler formula. Therefore, river discharge can be estimated with LSPIV along with a calibrated Manning's roughness, without additional bathymetry survey. The proposed new method is applied to measure discharge in Milwaukee River (Milwaukee, Wisconsin, U.S.A.), which agreed well with data obtained from a nearby streamgage station.     

Multi-Level Fusion Net for hand pose estimation in hand-object interaction

This work is about solving a challenging problem of estimating the full 3D hand pose when a hand interacts with an unknown object. Compared to isolated single hand pose estimation, occlusion and interference induced by the manipulated object and the clutter background bring more difficulties for this task. Our proposed Multi-Level Fusion Net focuses on extracting more effective features to overcome these disadvantages by multi-level fusion design with a new end-to-end Convolutional Neural Network (CNN) framework. It takes cropped RGBD data from a single RGBD camera at free viewpoint as input without requiring additional hand–object pre-segmentation and object or hand pre-modeling. Through extensive evaluations on public hand–object interaction dataset, we demonstrate the state-of-the-art performance of our method.     

Decomposition and replacement: Spatial knowledge distillation for monocular depth estimation

Knowledge distillation has become a key technique for making smart and light-weight networks through model compression and transfer learning. Unlike previous methods that applied knowledge distillation to the classification task, we propose to exploit the decomposition-and-replacement based distillation scheme for depth estimation from a single RGB color image. To do this, Laplacian pyramid-based knowledge distillation is firstly presented in this paper. The key idea of the proposed method is to transfer the rich knowledge of the scene depth, which is well encoded through the teacher network, to the student network in a structured way by decomposing it into the global context and local details. This is fairly desirable for the student network to restore the depth layout more accurately with limited resources. Moreover, we also propose a new guidance concept for knowledge distillation, so-called ReplaceBlock, which replaces blocks randomly selected in the decoded feature of the student network with those of the teacher network. Our ReplaceBlock gives a smoothing effect in learning the feature distribution of the teacher network by considering the spatial contiguity in the feature space. This process is also helpful to clearly restore the depth layout without the significant computational cost. Based on various experimental results on benchmark datasets, the effectiveness of our distillation scheme for monocular depth estimation is demonstrated in details. The code and model are publicly available at : https://github.com/tjqansthd/Lap_Rep_KD_Depth.     

Meeting the challenge of water sustainability: The role of process systems engineering

This white paper is the result of discussions during the FIPSE-4 conference ( http://fi-in-pse.org ) in June 2018. It aims to highlight open problems and provide directions for future research in the area of water with emphasis on its agricultural usages. Some of the open problems discussed are: (a) the use of ecosystems as unit operations to understand their role in providing freshwater and in cleaning polluted water; (b) consideration of interactions and independencies between flows of water and other resources, such as food, energy, materials, ecosystem services, and environmental emissions; (c) challenges in modeling, sensing, and closed-loop control in precision irrigation. In particular, the development of agro-hydrological models that balance computing speed versus solution details and accuracy: (d) The use of state and parameter estimation approaches, through field measurements, to obtain soil moisture levels accurately; and (e) decision support systems to administer water and nutrient needs for optimum yields of agricultural products.