Decomposition of first-order hybrid Petri nets for hierarchical control of manufacturing systems

Although hybrid Petri net (HPN) is a popular formalism in modelling hybrid production systems, the HPN model of large scale systems gets substantially complicated for analysis and control due to large dimensionality of such systems. To overcome this problem, a typical approach is to decompose the net into subnets and then control the plant through hierarchical or decentralized structures. Although this concept has been widely discussed in the literature for discrete PNs, there is a lack of research for HPNs. In this paper, a new method of decomposition of first-order hybrid Petri nets (FOHPNs) is proposed first and then the hierarchical control of the subnets through a coordinator is introduced. The advantage of using the proposed approach is validated by an existing example. A sugar milling case study is analysed by using a decomposed FOHPN model and the optimization results are compared against the results of the approaches presented in other papers. Simulation results show not only an improvement in production rate, but also show the ability to control the plant online. In addition, by using the hierarchical control structure for an FOHPN model, it is possible to reduce the cost of communication links, improve the reliability of the system, maintain the plant locally, and partially redesign the system.     

Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

The supervision of a hybrid power plant, including solar panels, a gas microturbine and a storage unit operating under varying solar power profiles is considered. The Economic Supervisory Predictive controller assigns the power references to the controlled subsystems of the hybrid cell using a financial criterion. A prediction of the renewable sources power is embedded into the supervisor. Results deteriorate when the solar power is unsteady, owing to the inaccuracy of the predictions for a long-range horizon of 10 s. The receding horizon is switched between an upper and a lower value according to the amplitude of the solar power trend. Theoretical results show the relevance of horizon switching, according to a tradeoff between performance and prediction accuracy. Experimental results, obtained in a Hardware In the Loop (HIL) framework, show the relevance of the variable horizon approach. Power amplifiers allow us to simulate virtual components, such as a gas microturbine, and to blend their powers with that of real devices (storage unit, real solar panels). In this case, fuel savings, reaching 15%, obtained under unsteady operating conditions lead to a better overall performance of the hybrid cell. The overall savings obtained in the experiments amount to 12%.     

Science fiction reduces the eeriness of android robots: A field experiment

As suggested by the uncanny valley hypothesis, robots that resemble humans likely elicit feelings of eeriness. Based on the psychological model of meaning maintenance, we expected that the uncanny valley experience could be mitigated through a fictional story, due to the meaning-generating function of narratives. A field experiment was conducted, in which 75 participants interacted with the humanlike robot Telenoid. Prior to the interaction, they either read a short story, a non-narrative leaflet about the robot, or they received no preliminary information. Eeriness ratings were significantly lower in the science fiction condition than in both other conditions. This effect was mediated by higher perceived human-likeness of the robot. Our findings suggest that science fiction may provide meaning for otherwise unsettling future technologies.     

Rectangular Section Control Technology for Silicon Steel Rolling

Rectangular section control technology(RSCT)was introduced to achieve high-precision profile control during silicon steel rolling.The RSCT principle and method were designed,and the whole RSCT control strategy was developed.Specifically,RSCT included roll contour design,rolling technology optimization,and control strategy development,aiming at both hot strip mills(HSMs)and cold strip mills(CSMs).Firstly,through the high-performance variable crown(HVC)work roll optimization design in the upper-stream stands and the limited shifting technology for schedule-free rolling in the downstream stands of HSMs,a hot strip with a stable crown and limited wedge,local spot,and single wave was obtained,which was suitable for cold rolling.Secondly,an approximately rectangular section was obtained by edge varying contact(EVC)work roll contour design,edge-drop setting control,and closed loop control in the upper-stream stands of CSMs.Moreover,complex-mode flatness control was realized by coordinating multiple shape-control methods in the downstream stands of CSMs.In addition,the RSCT approach was applied in several silicon-steel production plants,where an outstanding performance and remarkable economic benefits were observed.     

Effects of SO2 on selective catalytic reduction of NO with NH3 over a TiO2 photocatalyst

The effect of SO₂ gas was investigated on the activity of the photo-assisted selective catalytic reduction of nitrogen monoxide (NO) with ammonia (NH₃) over a TiO₂ photocatalyst in the presence of excess oxygen (photo-SCR). The introduction of SO₂ (300 ppm) greatly decreased the activity of the photo-SCR at 373 K. The increment of the reaction temperature enhanced the resistance to SO₂ gas, and at 553 K the conversion of NO was stable for at least 300 min of the reaction. X-ray diffraction, FTIR spectroscopy, thermogravimetry and differential thermal analysis, x-ray photoelectron spectroscopy (XPS), elemental analysis and N₂ adsorption measurement revealed that the ammonium sulfate species were generated after the reaction. There was a strong negative correlation between the deposition amount of the ammonium sulfate species and the specific surface area. Based on the above relationship, we concluded that the deposition of the ammonium sulfate species decreased the specific surface area by plugging the pore structure of the catalyst, and the decrease of the specific surface area resulted in the deactivation of the catalyst.     

TCBR-HMM: An HMM-based text classifier with a CBR system

This paper presents an innovative solution to model distributed adaptive systems in biomedical environments. We present an original TCBR-HMM (Text Case Based Reasoning-Hidden Markov Model) for biomedical text classification based on document content. The main goal is to propose a more effective classifier than current methods in this environment where the model needs to be adapted to new documents in an iterative learning frame. To demonstrate its achievement, we include a set of experiments, which have been performed on OSHUMED corpus. Our classifier is compared with Naive Bayes and SVM techniques, commonly used in text classification tasks. The results suggest that the TCBR-HMM Model is indeed more suitable for document classification. The model is empirically and statistically comparable to the SVM classifier and outperforms it in terms of time efficiency.     

Assessment of the different sources of uncertainty in a SWAT model of the River Senne (Belgium)

Although rainfall input uncertainties are widely identified as being a key factor in hydrological models, the rainfall uncertainty is typically not included in the parameter identification and model output uncertainty analysis of complex distributed models such as SWAT and in maritime climate zones. This paper presents a methodology to assess the uncertainty of semi-distributed hydrological models by including, in addition to a list of model parameters, additional unknown factors in the calibration algorithm to account for the rainfall uncertainty (using multiplication factors for each separately identified rainfall event) and for the heteroscedastic nature of the errors of the stream flow. We used the Differential Evolution Adaptive Metropolis algorithm (DREAM_(zs)) to infer the parameter posterior distributions and the output uncertainties of a SWAT model of the River Senne (Belgium). Explicitly considering heteroscedasticity and rainfall uncertainty leads to more realistic parameter values, better representation of water balance components and prediction uncertainty intervals.     

Friction compensation controller for load varying machine tool feed drive

The load applied to a machine tool feed drive changes during the machining process as material is removed. This load change alters the Coulomb friction of the feed drive. Because Coulomb friction accounts for a large part of the total friction the friction compensation control accuracy of the feed drives is limited if this nonlinear change in the applied load is not considered. This paper presents a new friction compensation method that estimates the machine tool load in real time and considers its effect on friction characteristics. A friction observer based on a Kalman filter with load estimation is proposed for friction compensation control considering the applied load change. A specially designed feed drive testbed that enables the applied load to be modified easily was constructed for experimental verification. Control performance and friction estimation accuracy are demonstrated experimentally using the testbed.     

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

黑龙江省节水增粮行动(2012—2015年)实施方案

文章通过对黑龙江省高效节水现状,存在问题的分析,结合黑龙江省高效节水发展的支撑能力,阐述黑龙江省节水增粮目标、任务及总体布局,为黑龙江省2012—2015年节水增粮实施提供依据。