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Energy efficiency evaluation of industrial park customers is carried out to help customers develop optimization strategies and improve the energy efficiency level. The evaluation indexes are proposed based on pressure-state-response (PSR) model and the redundant indexes are selected by combining principal component analysis (PCA) and correlation analysis. Thus the dynamic energy efficiency index system is constructed. The weight of indexes is calculated by the entropy weight method (EWM). An energy efficiency evaluation model based on improved Grey-technique for order preference by similarity to ideal solution (TOPSIS) is proposed, which replaces the Euclidean distance by weighted grey correlation degree and can accurately evaluate customers’ energy efficiency level. An energy efficiency optimization model is proposed, which can maximize optimization benefits while improving energy efficiency. The validity of the evaluation and optimization models is verified by the case study of an industrial park.

In home-based elderly care service, how to precisely recognize activities is a key issue in the design and implementation of context-aware service for elderly people. Existing research works reveal that those approaches ignore the characteristics of activity diversity, and similarity and the features of activities of elderly people at home, so recognition accuracy of those approaches are not high enough for real-life applications. Thus, in this paper, we first study the types of activities in home-based elderly care service. Then, we propose a two-stage elderly home activity recognition method based on random forest and activity similarity. The method uses improved random forest to obtain a preliminary result in the first stage. Then, the correlation between activity, location, and time is employed to judge the rationality of the result. The similarity of activities is further used to correct the results in the second stage. We set up a series of experiments to evaluate the effectiveness and efficiency of our approach.

Aiming at the comprehensive evaluation of energy efficiency for large industrial customers, an energy efficiency perception evaluation model based on the gray correlation degree and entropy weight method is proposed in this paper. Firstly, in order to perceive the dynamic energy efficiency of large industrial customers, an energy efficiency measurement model based on the gray correlation degree is established and the weight of energy efficiency evaluation index is determined by entropy weight method. By calculating the correlation between the original value and the optimal value of indexes, energy efficiency measurement results at different time points are obtained. Then, based on energy efficiency measurement results, energy efficiency evaluation results are calculated to determine the customer’s energy efficiency level. Finally, through the case study of a steel plant, the energy efficiency measurement and comprehensive evaluation of four key production links are analyzed, and it is found that the energy efficiency of sintering process 2 has room for improvement.

In order to reflect the energy efficiency level of industrial users, the power efficiency evaluation for users can provide basis for the implementation of effective energy efficiency improvement measures. Firstly, based on the pressure-state-response model, the comprehensive index system of energy efficiency evaluation is constructed, which considers the relationship between indicators from three dimensions. Then, an energy efficiency evaluation model based on TOPSIS is proposed. To solve the difficulty in determining the index weight of traditional TOPSIS method under multiple factors, a comprehensive weighting method combining AHP and entropy weight method is proposed to establish an improved TOPSIS evaluation model. Combined with subjective and objective evaluation information, it overcomes the limitation of traditional single weighting method. Finally, the energy efficiency evaluation model is used to analyze the energy efficiency level of key production links in an steel plant.

The sampling-based motion planner is the mainstream method to solve the motion planning problem in high-dimensional space. In the process of exploring robot configuration space, this type of algorithm needs to perform collision query on a large number of samples, which greatly limits their planning efficiency. Therefore, this paper uses machine learning methods to establish a probabilistic model of the obstacle region in configuration space by learning a large number of labeled samples. Based on this, the high-dimensional samples’ rapid collision query is realized. The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self-adaptive model training method based on Greedy expectation-maximization (EM) algorithm is proposed. At the same time, this method has the capability of online updating and can eliminate model fitting errors due to environmental changes. Finally, the model is combined with a variety of sampling-based motion planners and is validated in multiple sets of simulations and real world experiments. The results show that, compared with traditional methods, the proposed method has significantly improved the planning efficiency.

Intelligent vehicle includes perception, planning, control, decision making and interaction. Path planning and vehicle control act the vehicle directly, and its stability, safety and efficiency play an important role in intelligent driving systems. This article researches optimal path planning and vehicle tracking control capabilities in specific scenario, by the motion description of the vehicle kinematics model, establishing the out-parking map and creating reference path, combining with the model predictive control optimization algorithm and collaborating with sensors and devices which supported by a development project. By analyzing test data, all parts of designed can achieve the expected function, and the overall performance is good.

The ejectors equipped for anode and cathode recirculation loops are more reliable and low-cost in maintenance than blowers in a solid oxide fuel cell-gas turbine hybrid system. However, an effective control system is one of the technical challenges associated with the development of the hybrid system. In present work, a novel control strategy was designed to restrict the hybrid system equipped with anode and cathode recirculation ejectors to a safe and feasible zone. Six control loops were carried out to control the vital parameters including power, rotation speed, fuel utilization, anode and cathode inlet temperature, and turbine inlet temperature. At the same time, the performance of another control system without anode inlet temperature control loop was also investigated. A comparison results of the two control systems reveal that both anode and cathode inlet temperature loops are necessary. If there is no anode inlet temperature control loop, the system efficiency decreases by 0.18%, the fuel cell inlet temperature differences between anode and cathode increases by 9 K and the compressor surge margin decreases by 1.8% at 95% system load. It not only causes a little reduction in efficiency but also may cause some latent dangers of the hybrid system at a part load condition. Consequently, the control system with all six control loops is effective and appropriate.

A coupled numerical approach is investigated for predicting combustion instability limit cycle characteristics when the combustor contains a long flame. The test case is the ORACLES combustor, with a turbulent premixed flame a metre long: it exhibits limit cycle oscillations at ∼ 50 Hz and normalised velocity amplitude ahead of the flame of ∼ 0.29. The approach obtains the flame response to acoustic excitation using Large Eddy Simulations (LES), and couples this with a low-order wave-based network representation for the acoustic waves within the combustor. The flame cannot be treated as acoustically compact; the spatial distribution of both its response and the subsequent effect on the acoustics must be accounted for. The long flame is uniformly segmented axially, each segment being much shorter than the flow wavelengths at play. A series of “local” flame describing functions, one for the heat release rate response within each segment to velocity forcing at a fixed reference location, are extracted from the LES. These use the Computational Fluid Dynamics toolbox, OpenFOAM, with an incompressible approximation for the flow-field and combustion modelled using the Partially Stirred Reactor model with a global one-step reaction mechanism. For coupling with the low-order acoustic network modelling, compact acoustic jump conditions are derived and applied across each flame segment, while between flame segments, wave propagation occurs. Limit cycle predictions from the proposed coupled method agree well with those predicted using the continuous 1-D linearised Euler equations, validating the flame segmentation implementation. Limit cycle predictions (frequency 51.6 Hz and amplitude 0.38) also agree well with experimental measurements, validating the low-order coupled method as a prediction tool for combustors with long flames. A sensitivity analysis shows that the predicted limit cycle amplitude decreases rapidly when acoustic losses at boundaries are accounted for, and increases if combustor heat losses downstream of the flame are accounted for. This motivates more accurate determination of combustor boundary and temperature behaviour for thermoacoustic predictions.

This paper presents a character-level seque-nce-to-sequence learning method, RNNembed. This method allows the system to read raw characters, instead of words generated by preprocessing steps, into a pure single neural network model under an end-to-end framework. Specifically, we embed a recurrent neural network into an encoder–decoder framework and generate character-level sequence representation as input. The dimension of input feature space can be significantly reduced as well as avoiding the need to handle unknown or rare words in sequences. In the language model, we improve the basic structure of a gated recurrent unit by adding an output gate, which is used for filtering out unimportant information involved in the attention scheme of the alignment model. Our proposed method was examined in a large-scale dataset on an English-to-Chinese translation task. Experimental results demonstrate that the proposed approach achieves a translation performance comparable, or close, to conventional word-based and phrase-based systems.

Current transformers (CT) are theoretically feasible to be used as the energy harvester to supply power for monitoring devices laid on high-voltage power line. In order to maximize the collected power, the energy harvester with improved self-optimization control method is proposed. In this paper, a simulator-friendly model of CT energy harvester is built. The power characteristics of CT are analyzed whereby the parameters could be optimized. Besides, the control method employs improved perturbation and observation method (P&O) in which the step of the perturbation is changeable. Compared with traditional control method, the method is proved by simulation results that both tracking speed and little oscillation could be obtained.

This paper proposes a reversible data hiding scheme by exploiting the DGHV fully homomorphic encryption, and analyzes the feasibility of the scheme for data hiding from the perspective of information entropy. In the proposed algorithm, additional data can be embedded directly into a DGHV fully homomorphic encrypted image without any preprocessing. On the sending side, by using two encrypted pixels as a group, a data hider can get the difference of two pixels in a group. Additional data can be embedded into the encrypted image by shifting the histogram of the differences with the fully homomorphic property. On the receiver side, a legal user can extract the additional data by getting the difference histogram, and the original image can be restored by using modular arithmetic. Besides, the additional data can be extracted after decryption while the original image can be restored. Compared with the previous two typical algorithms, the proposed scheme can effectively avoid preprocessing operations before encryption and can successfully embed and extract additional data in the encrypted domain. The extensive testing results on the standard images have certified the effectiveness of the proposed scheme.

Finding feasible motion for robots with high-dimensional configuration space is a fundamental problem in robotics. Sampling-based motion planning algorithms have been shown to be effective for these high-dimensional systems. However, robots are often subject to task constraints (e.g., keeping a glass of water upright, opening doors and coordinating operation with dual manipulators), which introduce significant challenges to sampling-based motion planners. In this work, we introduce a method to establish approximate model for constraint manifolds, and to compute an approximate metric for constraint manifolds. The manifold metric is combined with motion planning methods based on projection operations, which greatly improves the efficiency and success rate of motion planning tasks under constraints. The proposed method Approximate Graph-based Constrained Bi-direction Rapidly Exploring Tree (AG-CBiRRT), which improves upon CBiRRT, and CBiRRT were tested on several task constraints, highlighting the benefits of our approach for constrained motion planning tasks.

In this work, a weakly nonlinear analytical model of the flame describing function (FDF) of a two-dimensional laminar premixed slit flame subjected to both transverse perturbations and two-dimensional mean flows has been proposed. Two oncoming velocity convection models are used to investigate their effects on the flame tip movements and the nonlinear FDFs. The perturbation method is then used to solve the G-equation. As the flame tip movement plays an important role in the transverse perturbed FDF, its analytical solution is also derived. Validations of these weakly nonlinear solutions are then conducted by comparing them to the previous linear flame transfer function for weak perturbations and results obtained numerically solving the G-equation model. Results show that the transverse oncoming velocity convection models have a significant effect on the nonlinear flame tip dynamics and the nonlinear FDFs, which can also be found in the previous linear model. The transverse nonlinear effects are not so large, as the effect of the transverse perturbation is typically smaller than that of the longitudinal perturbation, which still contributes to reveal the physical mechanisms of the transverse combustion instabilities. For strong transverse perturbations, the analytical solutions of the FDFs derived by the nonlinear model are different from those derived by the linear model both using the uniform model, especially at low frequencies, which indicates that the nonlinear effect is more significant at low frequencies. Transverse mean flow velocity and the amplitude of the transverse disturbed flow velocity can enhance the nonlinearities at low frequencies.

A new triple wing harmonium (TWH) model that integrates text metadata into a low-dimensional semantic space is proposed for the application of content-based movie recommendation. The text metadata considered here include movie synopsis, actor list, and user comments. We develop a new TWH model projecting these multiple textual features into low-dimensional latent topics with different probability distribution assumptions. A contrastive divergence (CD) algorithm is used for efficient learning and inference. Experimental results suggest that the proposed method performs better than the state-of-the-art algorithms for movie recommendation.

Heat release rate perturbations of two-dimensional asymmetric premixed slot flames subjected to transverse perturbations, so called flame transfer functions (FTFs), are theoretically studied in this paper. The proposed models of both the two-dimensional conical flame and V-flame are studied based upon the linear analysis. The flame front dynamic response under the transverse disturbance is captured and solved using the G-equation model. The analytical solution of the FTF is derived using different oncoming velocity perturbation models, in order to reveal the flame heat release rate response mechanism to the transverse disturbances. In the two-dimensional conical flame model, the flame front tip moves with the transverse perturbations, which in turn also affects the FTF. In the two-dimensional V-flame model, the flame front tips are intersected by the flame branches and the unburned gas boundaries; this also contributes to the different behaviours of the FTF. The linear flame tip dynamics and the explicit analytical models of the FTFs of both the conical flame model and the V-flame model are proposed and validated. Results show that the transverse disturbance propagation velocity model has a significant effect on the FTF. The movement of the flame tip is also affected by the transverse convection models.

This work performs a theoretical and numerical analysis of the communication between one-dimensional acoustic and entropy waves in a duct with a mean temperature gradient. Such a situation is highly relevant to combustor flows where the mean temperature drops axially due to heat losses. A duct containing a compact heating element followed by an axial temperature gradient and choked end is considered. The proposed jump conditions linking acoustic and entropy waves on either side of the flame show that the generated entropy wave is generally proportional to the mean temperature ratio across the flame and the ratio [Formula: see text], where [Formula: see text] is the flame transfer function. It is inversely proportional to the Mach number immediately downstream of the flame M2. The acoustic and entropy fields in the region of axial mean temperature gradient are calculated using four approaches: (1) using the full three linearised Euler equations as the reference; (2) using two linearised Euler equations in which the acoustic and entropy waves are assumed independent (thus allowing the extent of communication between the acoustic and entropy wave to be evaluated); (3) using a Helmholtz solver which neglects mean flow effects and (4) using a recently developed analytical solution. It is found that the communication between the acoustic and entropy waves is small at low Mach numbers; it rises with increasing Mach number and cannot be neglected when the mean Mach number downstream of the heating element exceeds 0.1. Predictions from the analytical method generally match those from the full three linearised Euler equations, and the Helmholtz solver accurately determines the acoustic field when [Formula: see text].

Accurately predicting the thermoacoustic modes of a combustor depends upon knowledge of the thermodynamic properties within the combustor; flame temperature, heat release rate, speed of sound and ratio of specific heats all have a strong effect. Calculating the global equilibrium properties resulting from fuel combustion is not straightforward due to the presence of complex multi-species and multi-step reaction mechanisms. A method which decouples the calculations of species dissociations is proposed in this work: this improves the precision of calculation when using few species and reduces the computational cost and complexity to a degree that embedding within low order thermoacoustic network codes is feasible. When used to calculate the combustion product mole fractions, temperature, heat release rate, speed of sound and ratio of specific heats for hydrocarbon-air flames, the method is found to be accurate and highly efficient across different operating conditions and fuel types. The method is then combined with improved low-order wave-based network modelling, the latter employing wave-based acoustic models which account for the variation of thermodynamic properties along the combustion chamber. For a laboratory-scale combustor with a large downstream temperature variation, it is shown that accurate prediction of thermoacoustic modal frequencies and growth rates does depend on accounting for the variation in thermodynamic properties.

This paper presents a character-level sequence-to-sequence learning method, RNNembed. Specifically, we embed a Recurrent Neural Network (RNN) into an encoder-decoder framework and generate character-level sequence representation as input. The dimension of input feature space can be significantly reduced as well as avoiding the need to handle unknown or rare words in sequences. In the language model, we improve the basic structure of a Gated Recurrent Unit (GRU) by adding an output gate, which is used for filtering out unimportant information involved in the attention scheme of the alignment model. Our proposed method was examined in a large-scale dataset on a task of English-to-Chinese translation. Experimental results demonstrate that the proposed approach achieves a translation performance comparable, or close, to conventional word-based and phrase-based systems.

International audience; The article presents a new method to measure heat release rate disturbances from flames when optical access is limited. The technique is based on the determination of the travel time of ultrasonic waves propagating through the flow. The link between heat release rate and sound travel time disturbances depends on the configuration considered. An expression is established here for the case of unconfined premixed flames driven by buoyancy forces associated with the Kelvin-Helmholtz instability formed by the interaction between accelerating hot burned gases and cold ambient air at rest. The system and the principle used for the determination of the sound travel time are then presented and validated under nonreacting conditions. Effects of the main parameters on the precision of this detection technique are examined experimentally. Measurements in reacting conditions are compared to heat release rate data obtained with another technique based on the chemiluminescence emission. A good agreement is obtained between both signals for the different cases explored demonstrating the sensitivity of the proposed technique.