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In applications such as target detection, domain knowledge of sensed data is often available. In this paper, we incorporate the available domain knowledge into clustering process and develop a knowledge-driven Mahalanobis distance-based ART (adaptive resonance theory) clustering algorithm. The strength of the knowledge-driven algorithm is that it can automatically determine the number of clusters with improved clustering results. The validity of the new algorithm has been verified on four artificial datasets. In addition, the algorithm has been adopted in our cognition-inspired system for clustering data stream, where known target library and dispersion of feature or attributes are available. The basic idea of this system is to divide data stream into frames, and to incorporate knowledge learned in previous frames into clustering of the following ones. Experimental studies have demonstrated that the evolving learning mechanism leads to improved clustering results compared with conventional incremental clustering algorithm Fuzzy ART and batch-based clustering algorithm k-means.
A novel continuous-flow electrochemical reactor was designed for oilfield wastewater advancedtreatment. Magnetic steel slag particles were prepared as particle electrodes in the reactor. The bestpreparation condition of steel slag particle electrodes is obtained via orthogonal and single factorexperiments. The resulting samples were characterized by scanning electron microscope and vibratingsample magnetometer. It was demonstrated that saturation magnetic intensity of the particle was1.6389 emu/g. The process parameters and the total organic carbon (TOC) removal kinetics wereinvestigated. Result showed that the reactor could remove over 85% TOC in 2 h, which was considered tobe effective.
In order to improve the accuracy of forecasts of the electricity sales of power sales companies, a depth forecast model of electricity sales based on the characteristics of the power market is proposed. First, based on survival analysis, the calculation method of the user churn rate in the electricity market is given, and the number of users at a certain moment in the future is predicted. Then, users' electricity consumption that calculated by the deep belief network and the predicted quantity of users are combined to design a forecast model of electricity sales. Finally, the model is solved utilizing the weighting algorithm of adaptive inertia. The analysis of the example shows that the proposed method achieves a signifi cant improvement in the accuracy of power sales forecasting.
Catalytic particle electrodes (CPEs) were developed from steel slag waste, and were used to degrade Rhodamine B (RhB). To improve degradation efficiency, Mn-loaded CPEs with good reproductive performance were constructed through ultrasound impregnation–calcination strategy. The resulting samples were characterized by XRF, SEM, EDS, XPS and XRD. Degradation efficiency of the systems with Mn-loaded CPEs were 93.22% without air supply in 80 min. And degradation efficiency for Mn-loaded CPEs reached 100% with air supply in 50 min. Furthermore, the enhanced mechanism was proposed. The high degradation efficiency could be ascribed to the increase of hydroxyl radicals originated from electro-Fenton.
The problem of load fuctuation in the distribution network and increasing power grid cost input caused by the unpredictable behavior of electric vehicle (EV) users in response to electricity price is investigated in this paper. An optimization model method for the charging and discharging price of electric vehicles is proposed, considering the vehicle owner response and power grid cost. The rule of EV user travel is frst analyzed, and the travel and battery state constraints are defned. Under the constraints of user charging and discharging behavior and battery characteristics, a user transfer rate and unit energy cost function is designed to construct a multi-objective model of charging and discharging price that minimizes electricity expenditure and avoids an increase in power grid investment. Finally, an improved multi-target fsh swarm algorithm is presented to solve the model optimization problem. The example analysis shows that the proposed method can reduce the peak-valley load diference of the system and cost input of the power grid, as well as provide users with regulation ability to access the power grid at diferent time periods
Aiming at the inaccuracy of short-term electricity price forecasting in competitive power markets, a probabilistic short-term electricity price forecasting method based on the quantile neural network model is proposed. First, a method for selecting electricity price similarity based on comprehensive infuencing factors is designed to select the forecast data set with similar characteristics to the forecast date. The similar daily quantile regression algorithm is then combined with the generalized dynamic fuzzy neural network to construct a quantile neural network electricity price model for obtaining the predicted daily electricity price condition quantile. Finally, the kernel density function is used to convert the predicted daily electricity price condition quantile into the predicted probability density curve to realize short-term electricity price probability prediction. The data of the electricity market of the city of Dayton, Ohio in the United States is used as an example. The experimental results demonstrate that the proposed method can efectively improve the accuracy of short-term electricity price forecasting
A thermo-responsive polymer (PNNB) wassynthesized with lower critical solution temperature 27.5ºCand over 95% recovery. The adsorption of porcine pancreaticlipase on Cibacron Blue F3GA-conjugated PNNB (PNNBCB)closely followed the bi-Langmuir adsorption isotherm. The maximum adsorption capacity was found at pH 5.0,with a ligand density of 18.4 μmol/g polymers. Theoptimized eluent was a 0.01 M phosphate buffer solution atpH 8.0 containing 20% ethylene glycol. Six adsorptiondesorptionrecycles indicated excellent reusability of theaffinity adsorbent. PNNB-CB was applied to separate porcinepancreatic lipase from its crude material giving a lipaseactivity recovery of 81.6% with a 16-fold purificationfactor. Lipase could be purified to single-band purity,according to gel electrophoresis. The purification strategyis therefore feasible and efficient for purifying proteins ofinterest.
With the wide application of intelligent household appliances, the optimization of electricity behavior has become an important component of home-based intelligent electricity. In this study, a multi-objective optimization model in an intelligent electricity environment is proposed based on economy and comfort. Firstly, the domestic consumer's load characteristics are analyzed, and the operating constraints of interruptible and transferable electrical appliances are defined. Then, constraints such as household electrical load, electricity habits, the correlation minimization electricity expenditure model of household appliances, and the comfort model of electricity use are integrated into multi-objective optimization. Finally, a continuous search multi-objective particle swarm algorithm is proposed to solve the optimization problem. The analysis of the corresponding example shows that the multi-objective optimization model can effectively reduce electricity costs and improve electricity use comfort.
In the present work, tin incorporated MCM-48 catalysts were prepared by different strategies andapplied in the synthesis of MB-AC and MBOH. The performance of the catalysts is strongly affected bymesochannel environment and acid texture. The organic-graft method (Sn-G-MCM-48) can modify themesochannel environment and enhance the acid intensity and concentration (0.28 mmol/gcat),resulting in the improvement of the catalytic activity to the target reactions. The total yield of MB-AC andMBOH is as much as 68.4% over Sn-G-MCM-48 under mild conditions.
Due to unfortunate mistake the grant numbers have been omitted in the acknowledgments section: This work is supported by the National Natural Science Foundation of China (No. 51437003), Jilin Province Science and Technology Development Plan Project of China (20160623004TC, 20180201092GX), Jilin Science and Technology Innovation Development Plan Project of China (201830817).