Extracting Multiple Nodes in a Brain Region of Interest for Brain Functional Network Estimation and Classification

Purpose: Brain functional networks (BFNs) has become important approach for diagnosis of some neurological or psychological disorders. Before estimating BFN, obtaining blood oxygen level dependent (BOLD) representative signals from brain regions of interest (ROIs) is important. In the past decades, the common method is generally to take a ROI as a node, averaging all the voxel time series inside it to extract a representative signal. However, one node does not represent the entire information of this ROI, and averaging method often leads to signal cancellation and information loss. Inspired by this, we propose a novel model extraction method based on an assumption that a ROI can be represented by multiple nodes. Methods: In this paper, we first extract multiple nodes (the number is user-defined) from the ROI based on two traditional methods, including principal component analysis (PCA), and K-means (Clustering according to the spatial position of voxels). Then, canonical correlation analysis (CCA) was issued to construct BFNs by maximizing the correlation between the representative signals corresponding to the nodes in any two ROIs. Finally, to further verify the effectiveness of the proposed method, the estimated BFNs are applied to identify subjects with autism spectrum disorder (ASD) and mild cognitive impairment (MCI) from health controls (HCs). Results: Experimental results on two benchmark databases demonstrate that the proposed method outperforms the baseline method in the sense of classification performance. Conclusions: We propose a novel method for obtaining nodes of ROId based on the hypothesis that a ROI can be represented by multiple nodes, that is, to extract the node signals of ROIs with K-means or PCA. Then, CCA is used to construct BFNs.

Data mining algorithm and framework for identifying HVAC control strategies in large commercial buildings

Heating,Ventilation,and Air-Conditioning(HVAC)control strategies are set arbitrarily in many commercial buildings by operators,who sometimes lack relevant skills and professional training.It is acknowledged that improving the control strategy of HVAC is feasible and valid,which as a consequence can improve the overall HVAC performance of existing buildings.However,it is quite difficult for an outsiders or a commissioning agent to tell what the HVAC control strategies are and whether they are implemented appropriately in existing buildings.This paper is intended to carry out analysis on the data about Building Automation System(BAS),as well as the data about building energy,for the purpose of identifying the control strategies of HVAC in a given building by using data mining algorithm.Then the results can be adopted by us to determine whether the building is under faulty operation or is running under suboptimal conditions.In this paper,what are proposed are algorithms of data mining identification for some specific HVAC control strategies,including DR on/off strategy,DR reset strategy and temperature reset strategy of chilled water.On the basis of data mining algorithms,a framework is then developed so as to identify these strategies,and the main scenario of this identification framework is known as analyzing many commercial buildings on an energy monitoring platform of a public building.This framework takes the sensor data obtained from HVAC,including temperature,flowrate,and electricity usage,as input,which is followed by the application of Image Segmentation and PCA algorithm for preprocessing.Then,based on these input variables,XGBoost algorithm is employed to determine whether these strategies have been implemented in buildings or not.In order to get the data for training and testing the framework,EnergyPlus Runtime Language is adopted for the application of different strategies.t is finally shown by the result that the identification algorithm can achieve the accuracy rate of 92.5%in the case studies by using one-day operation data,and the identification algorithm can arrive at the accuracy rate of 100%by using three-day operation data.

Boosting the electrochemical performance of LiNi_(0.6)Mn_(0.2)Co_(0.2)O_2 through a trace amount of Mg-B co-doping

The extended cycle life of cells is often sacrificed at the expense of high specific energy for high-nickel materials.Cation doping is a promising method to build high-nickel cathode with high energy density and long cycle life.Herein,a trace amount of Mg-B co-doping in LiNi_(0.6)Mn_(0.2)Co_(0.2)O_2(NMC622)is investigated in this work,which shows improved structural and electrochemical stability of 1%Mg-0.5%B co-doped material at both 30 and 55℃in coin-cell.Comprehensive chemical composition,structural,and surface analysis are carried out in this paper.It was found that all the selected materials have a similar composition to the target.Moreover,Mg and B doping have different effects on the crystal structural change of NMC622,to be more specific,the c-lattice parameter increases with Mg doping,while the Li^(+)/Ni^(2+)mixing content increases when B was incorporated into the lattice.Furthermore,the microstructure of primary particles was changed by B doping significantly as confirmed by the SEM images.There were marginal benefits in terms of structural and electrochemical stability of materials introduced by Mg or B sole doping.In comparison,incorporating a suitable amount of both Mg and B into NMC622,we found the capacity retention of cells was noticeably improved by reducing the impedance growth and preventing cation mixing during cycling.This study demonstrates the importance of co-incorporation of Mg,B,and optimizing the co-dopant content to stabilize NMC622 as cathode for lithium-ion batteries.