No abstract is provided for this article.
This paper is concerned with the problem of multitarget coverage based on probabilistic detection model. Coverage configuration is an effective method to alleviate the energy-limitation problem of sensors. Firstly, considering the attenuation of node’s sensing ability, the target probabilistic coverage problem is defined and formalized, which is based on Neyman-Peason probabilistic detection model. Secondly, in order to turn off redundant sensors, a simplified judging rule is derived, which makes the probabilistic coverage judgment execute on each node locally. Thirdly, a distributed node schedule scheme is proposed for implementing the distributed algorithm. Simulation results show that this algorithm is robust to the change of network size, and when compared with the physical coverage algorithm, it can effectively minimize the number of active sensors, which guarantees all the targets<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>γ</mml:mi></mml:mrow></mml:math>-covered.
This paper presents a series of estimations performed in order to establish the actual cost-effectiveness of three different small wind turbines (SWTs) design solutions. Each of them was evaluated and based on their power curves and installation costs, using wind data from a numerical weather prediction (WNP) model, a return on investment (ROI) period was calculated. The chosen turbines are: a standard three bladed horizontal axis wind turbine (HAWT), an advanced diffuser augmented HAWT and a Darrieus type vertical axis wind turbine (VAWT). The conclusions drawn from this study entertain the idea that from the economical point of view, a price reduction of SWT systems is more important than aerodynamic complexity and efficiency.
Industry 5.0 aims to prioritize the needs and capabilities of human operators and design and implement production environments that support their role. Indeed, this paper addresses the need for a human-centered framework proposing a preference-based optimization algorithm in a human-robot collaboration (HRC) scenario with an ergonomics assessment to improve working conditions. The HRC application consists of optimizing a collaborative robot end-effector pose during an object-handling task. The approach utilizes an Active multi-Preference Learning (AmPL) algorithm, a preferencebased optimization method, where the user is requested to iteratively provide qualitative feedback by expressing pairwise preferences between a couple of candidates. To address physical well-being, an ergonomic performance index (RULA) is combined with the user’s pairwise preferences, so that the optimal setting can be computed. Experimental tests have been conducted to validate the method, involving collaborative assembly during the object handling performed by the robot. Results illustrate that the proposed method can improve the physical workload of the operator while easing the collaborative task.
The Dielectric Electro Active Polymer's (DEAP's) sensing capabilities is one of the main trio-characteristics of the material applicable area's, the trio-formations as applicable use are actuator, transducer and last but not least sensor. It is noted here that one of the main value propositions whenever DEAP material is used is the dual characteristics as the sensing/actuating capability. In the following work, the DEAP membrane will be modeled and the relation between the key variables (pressure & capacitance) will be determined. Hence, such a relation depends on the geometrical shape of the used membrane. So on, conceptualization is carried out to propose alternative solution for the sensor design using the DEAP and the laminate material. In general, the DEAP material has proven to be a very good sensor for pressure taking the advantage of flexibility, wide range of operation and last but not least the sensitivity. The theoretical model is benchmarked against the acquired data from the tests, good correlation has been recorded. The desired requirements for accuracy and measuring intervals are satisfied, showing a promising potentials for the DEAP material in pressure sensing in general, and pressure sensing application in specific.
In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark. The main challenges of the wind turbine fault detection lie in its nonlinearity, unknown disturbances as well as significant measurement noise. To overcome these difficulties, a data-driven fault detection scheme is proposed with robust residual generators directly constructed from available process data. A performance index and an optimization criterion are proposed to achieve the robustness of the residual signals related to the disturbances. For the residual evaluation, a proper evaluation approach as well as a suitable decision logic is given to make a correct final decision. The effectiveness of the proposed approach is finally illustrated by simulations on the wind turbine benchmark model.
The humidity sensitive characteristics of the sensor fabricated from 10 mol% La 2 O 3 doped CeO 2 nanopowders with particle size 17.26 nm synthesized via hydrothermal method were investigated at different frequencies. It was found that the sensor shows high humidity sensitivity, rapid response-recovery characteristics, and narrow hysteresis loop at 100 Hz in the relative humidity range from 11% to 95%. The impedance of the sensor decreases by about five orders of magnitude as relative humidity increases. The maximum humidity hysteresis is about 6% RH, and the response and recovery time is 12 and 13 s, respectively. These results indicate that the nanosized La 2 O 3 doped CeO 2 powder has potential application as high-performance humidity sensor.
Multi-standard software-definable radios which are capable of operation according to a variety of different mobile radio standards represent an extremely powerful tool for evolution towards future third-generation cellular systems. This is particularly the case in Europe where the emergence of advanced UMTS air-interfaces needs to be accompanied with some degree of backward compatibility with the well-established GSM/DCS systems. This paper examines a number of the architectural issues and trade-offs involved in the design of wideband multi-standard GSM/UMTS digital radios and presents an examination of the filtering and ADC technology requirements for their implementation. This work has been undertaken in the context of the FIRST project (Flexible Integrated Radio System Technology) as part of the ACTS mobile line.
This paper is concerned with the control issue for a class of networked control systems (NCSs) with packet dropouts and time-varying delays. Firstly, the addressed NCS is modeled as a Markovian discrete-time switched system with two subsystems; by using the average dwell time method, a sufficient condition is obtained for the mean square exponential stability of the closed-loop NCS with a desired disturbance attenuation level. Then, the desired controller is obtained by solving a set of linear matrix inequalities (LMIs). Finally, a numerical example is given to illustrate the effectiveness of the proposed method.
Input-to-state stability of nonlinear control system is described in several different manners, and has been a central concept since the equivalences among them were verified. In this paper, a framework of stability and dissipativity for stochastic control systems is constructed on the maximal existence interval of behaviors (states and external inputs), by the aid of stochastic Barbalat lemma and stochastic dissipativity. The main work consists of three aspects. First, input-to-state stability and robust stability are extended to the stochastic case, and several criteria are established. Second, two forms of dissipativity and their criteria are presented. Third, the key relations among the definitions of stability and dissipativity are verified. Compared with the existing results on stochastic input-to-state stability, our methods allow for non-globally Lipschitz condition, dynamic inputs without knowledge about boundedness and non-smoothness of storage functions, which are more effective and convenient to be used in practice.
No abstract is provided for this article.
Advanced manufacturing is a new trend for sustainable industrial development, and digital twin is a new technology that has attracted attention. Blast furnace smelting is an effective method in the manufacturing of iron and steel. Comprehensive and dependable surveillance of the blast furnace smelting process is essential for ensuring the smooth operation and improving of iron and steel output quality. The current technology makes it difficult to monitor the entire process of blast furnace ironmaking. Based on Unity 3D, this study presents a digital-twin virtual reality simulation system of blast furnace ironmaking. First, shading modeling creates a three-dimensional dynamic geometric model in different ironmaking system scenarios. Then, we script the animation and call particle system according to the motion mode of distinct geometric objects to give the dynamic effect of geometric objects. Shaders are the focus of the design and contributions. In addition, shader optimization technology can reduce hardware resource consumption and increase system fluency. Vertex shaders are used for all types of coordinate space transformation and vertex output; fragment shaders are used for texture sampling, light model calculation, normal calculation, noise superposition, and color output. The shader rendering technique allows for more realistic lighting effects. The presented dynamic digital twin system implements more realistic lighting analyzed in the ironmaking process. Virtual interaction logic’s design and deployment process is based on HTC VIVE hardware and VRTK toolkit. In the actual simulation process, the typical animation frame rate is stable at about 75 FPS (frames per second). The simulation system runs smoothly and a cutting-edge and state-of-the-art method for observing the blast furnace ironmaking process is suggested.
No abstract is provided for this article.
How to seek the important nodes of complex networks in product research and development (R&D) team is particularly important for companies engaged in creativity and innovation. The previous literature mainly uses several single indicators to assess the node importance; this paper proposes a multiple attribute decision making model to tentatively solve these problems. Firstly, choose eight indicators as the evaluation criteria, four from centralization of complex networks: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality and four from structural holes of complex networks: effective size, efficiency, constraint, and hierarchy. Then, use fuzzy analytic hierarchy process (AHP) to obtain the weights of these indicators and use technique for order preference by similarity to an ideal solution (TOPSIS) to assess the importance degree of each node of complex networks. Finally, taking a product R&D team of a game software company as a research example, test the effectiveness, operability, and efficiency of the method we established.
