Impact of electrolyte additives (alkali metal salts) on the capacitive behavior of NiO-based capacitors

Yong Zhang,Lizhen Wang,Aiqin Zhang,Yanhua Song,Xiaofeng Li,Xingbing Wu,Peipei Du,Lv Yan 한국화학공학회 2011 Korean Journal of Chemical Engineering Vol.28 No.2

To improve the specific capacitance and energy density of electrochemical capacitor, nanostructured NiO was prepared by high temperature solid-state method as electrode material. The crystal structure and morphology of as-parepared NiO samples were investigated by X-ray diffraction (XRD) and scanning electron microscopy (SEM). Cyclic voltammetry (CV) measurement was applied to investigate the specific capacitance of the NiO electrode. Furthermore,a novel mixed electrolyte consisting of NaOH, KOH, LiOH and Li_2CO_3 was prepared for the NiO capacitor,and the component and concentration of the four different electrolytes was examined by orthogonal test. The results showed that the NiO sample has cubic structure with nano-size particles, and the optimal composition of the electrolyte was: NaOH 2 mol L^(−1), KOH 3 mol L^(−1), LiOH 0.05 mol L^(−1), and Li_2CO_3 0.05 mol L^−1. At a scan rate of 10 mV s^(−1), the fabricated capacitor exhibits excellent electrochemical capacitive performance, while the specific capacitance and the energy density were 239 F g^(−1) and 85 Wh kg^(−1), which was higher than one-component electrolyte.

A novel relational regularization feature selection method for joint regression and classification in AD diagnosis

Zhu, Xiaofeng,Suk, Heung-Il,Wang, Li,Lee, Seong-Whan,Shen, Dinggang Elsevier 2017 Medical image analysis Vol.38 No.-

<P><B>Abstract</B></P> <P>In this paper, we focus on joint regression and classification for Alzheimer’s disease diagnosis and propose a new feature selection method by embedding the relational information inherent in the observations into a sparse multi-task learning framework. Specifically, the relational information includes three kinds of relationships (such as feature-feature relation, response–response relation, and sample-sample relation), for preserving three kinds of the similarity, such as for the features, the response variables, and the samples, respectively. To conduct feature selection, we first formulate the objective function by imposing these three relational characteristics along with an ℓ<SUB>2,1</SUB>-norm regularization term, and further propose a computationally efficient algorithm to optimize the proposed objective function. With the dimension-reduced data, we train two support vector regression models to predict the clinical scores of ADAS-Cog and MMSE, respectively, and also a support vector classification model to determine the clinical label. We conducted extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to validate the effectiveness of the proposed method. Our experimental results showed the efficacy of the proposed method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state-of-the-art methods.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A novel graph feature selection method for the AD/MCI diagnosis. </LI> <LI> A novel regularization exploiting the relational information inherent in the observations. </LI> <LI> First work considering three relationships for joint classification and regression. </LI> <LI> High accuracy of 95.7% for AD classification and 79.9% for MCI classification. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

Study on a Classification Model of Data Stream based on Concept Drift

Li Xiaofeng,Gao Weiwei 보안공학연구지원센터 2014 International Journal of Multimedia and Ubiquitous Vol.9 No.5

Research and Application of Data Mining and NARX Neural Networks in Load Forecasting

Li Xiaofeng,Yang Chunshan 보안공학연구지원센터 2014 International Journal of Database Theory and Appli Vol.7 No.2

Physical properties of tetragonal transition-metal borides Nb2MB2 (M = Mo, W, Re or Os) with a new superstructure

Xiaofeng Li,Yaping Tao,Ziyu Hu,Shengli Zhang 한국물리학회 2015 Current Applied Physics Vol.15 No.9

The mechanical and thermodynamic properties, chemical bonding characteristics and electronic structure of Nb2MB2 (M = Mo, W, Re or Os) with a new tetragonal U3Si2-type superstructure (space group P4/ mnc, no. 128) were studied by means of density functional theory calculations. All Nb2MB2 structures studied were demonstrated to be thermodynamically and mechanically stable. The bulk, shear and Young's moduli, Poisson's ratio, Debye temperature and anisotropy factors were derived for ideal polycrystalline Nb2MB2 aggregates. Among these compounds, Nb2WB2 was found to have the highest shear modulus and hardness. The electronic densities of state and electronic localization function analysis revealed the metallicity and strong covalent BeB, NbeB and MB bonding in Nb2MB2. Moreover, these results reveal that the covalence between Nb 4d, M nd (n = 4 for Mo and 5 for W, Re and Os) and B 2p states is the cause of the relatively higher elastic modulus and hardness of the Nb-based compounds. Finally, thermodynamic properties, including the bulk modulus, heat capacity and thermal expansion coefficient of Nb2WB2 were obtained systematically under high temperature and pressure.

Prediction of Traffic Flow Combination Model Based on Data Mining

Xiaofeng Li,Weiwei Gao 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.6

It is an important to quickly and accurately forecasting road network traffic flow in intelligent transportation systems, Aiming at the forecasting problem of short-term traffic flow, this paper proposed a traffic flow prediction algorithm, which based on traffic flow sequence partition and neural network model. Firstly, the algorithm divided the traffic flow into different patterns and time sequence by clustering, secondly, described and predicted traffic flow model according to BP neural network. Finally, the experiment shows that based on combined model is much accurate.

Fusion Trust Relation and Rating Data Algorithm

Xiaofeng Li 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.4

A new algorithm FTRA has been proposed, which infuses users’ trust network and rating data. The sparse problem of rating data will significantly reduce the accuracy of collaborative filtering recommendation. In addition to the users’ ratings data on the Internet, other data sources which can be used in the process of recommend, and one of the more common is trust network data which describes the mutual relationship between users. To solve this problem, this paper will the data of trust network as an important supplement on the rating data, and bases on graph theory concepts or methods, the similarity method in the paper, and the Katz method which is used to calculate the similarity of link, proposes the FTRA algorithm which organic infuses this two data, and then better to solve the sparse problem of the rating data faced by collaborative filtering. The experimental results on the Epinions dataset show that the FTRA algorithm is superior to or significantly better than the comparison algorithms, which include the algorithms that only based on the rating data or the trust relationship, and the other algorithms infusing the two data sources.

Recommendation Model Optimization Based on Diversity

Xiaofeng Li 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.5

The accuracy of the traditional online recommendation system, much depends on the collaborative filtering recommendation algorithm, however, recommend system aims to attract the interest of consumers and turn visitors into buyers, rather than accurately predict their score. Online recommendation system is the service version of social filtering process. Most previous studies emphasize the accuracy of the collaborative filtering algorithm. However, the effective recommendation system must be credible. It requires that the system logic be transparency and the system be able to provide consumers a new, inexperienced item. Based on the above, this paper proposes to research the quality evaluation of recommendation system from the angle of user’s experience, adding a freshness parameters of Top-N recommend collaborative filtering similarity calculation method, and comparing with the classical recommended algorithm. The experiment result has a certain degree of accuracy and high diversity, which provides basis for establishing the e-commercial recommendation system.

Electrochemical properties of Bi-Ni and Bi-Ni-Mn composite-coated electrolytic manganese dioxide

Xiaofeng Li,Zhigang Huang,Tongchi Xia,Huichao Dong,Yanghua Song 한국화학공학회 2014 Korean Journal of Chemical Engineering Vol.31 No.6

The Bi-Ni and Bi-Ni-Mn composite is separately coated on the surface of commercial electrolytic manganesedioxide (EMD) by using a simple chemical precipitation/oxidation method. The results of X-ray diffractionshow that a structure of γ-MnO2 is kept for all the coated EMD, but the intensity of their diffraction peaks is lower thanuncoated one. Both the Bi0.5-Ni0.5 and Bi0.35-Ni0.35-Mn0.3 composite benefits the discharge capacity and high-power performanceof the EMD electrodes. On the other hand, the results of scanning electron microscopy and energy dispersivespectroscopy confirm the more uniform distribution of the Bi0.15-Ni0.55-Mn0.3 composite on the surface of EMD thanthe Bi0.5-Ni0.5 one, thereby resulting in better cyclability of the electrodes. After 50 cycles at a 1C rate, the capacity retentionrate of the Bi0.15-Ni0.55-Mn0.3 composite-coated electrode reaches to 80%, which is far larger than the un-coated(49%) and the Bi0.5-Ni0.5 composite-coated (63%) one.

Fuzzy system and Improved APIT (FIAPIT) combined range-free localization method for WSN

( Xiaofeng Li ),( Liangfeng Chen ),( Jianping Wang ),( Zhong Chu ),( Qiyue Li ),( Wei Sun ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.7

Among numerous localization schemes proposed specifically for Wireless Sensor Network (WSN), the range-free localization algorithms based on the received signal strength indication (RSSI) have attracted considerable research interest for their simplicity and low cost. As a typical range-free algorithm, Approximate Point In Triangulation test (APIT) suffers from significant estimation errors due to its theoretical defects and RSSI inaccuracy. To address these problems, a novel localization method called FIAPIT, which is a combination of an improved APIT (IAPIT) and a fuzzy logic system, is proposed. The proposed IAPIT addresses the theoretical defects of APIT in near (it`s defined as a point adjacent to a sensor is closer to three vertexes of a triangle area where the sensor resides simultaneously) and far (the opposite case of the near case) cases partly. To compensate for negative effects of RSSI inaccuracy, a fuzzy system, whose logic inference is based on IAPIT, is applied. Finally, the sensor`s coordinates are estimated as the weighted average of centers of gravity (COGs) of triangles` intersection areas. Each COG has a different weight inferred by FIAPIT. Numerical simulations were performed to compare four algorithms with varying system parameters. The results show that IAPIT corrects the defects of APIT when adjacent nodes are enough, and FIAPIT is better than others when RSSI is inaccuracy.