Match Degrees Research Articles

Prediction of Photovoltaic Output Power Based on Match Degree and Entropy Weight Method

In this study, an algorithm, which had combined the match degree and entropy weighting method, was proposed to predict the efficiency of the photovoltaic output power. First, the key characteristic quantities were selected by the correlation analyses of output power history data and meteorological data of the photovoltaic power generation system. Second, according to the Euclidean distances between the predicted points and the historical data points, a method for the selections of similar sample points was proposed based on point-to-point theory. Finally, the match degrees between each characteristic quantity of the prediction points and the similar sample points were determined using the Mamdani reasoning method in fuzzy mathematics. The fitting weighting was then solved using the entropy weighting method, which was applied to fit the match degrees of the same characteristic quantities between the similar sample points. Next, the correlation coefficients were utilized as the weighting to fit the match degrees of the different characteristic quantities, resulting in a total match degree. This total match degree was then used to fit the photovoltaic output power of the similar sample points to the prediction points. We had proven that this method could have effective predictions and good adaptabilities in various weather statuses with high accuracy and real-time performance, especially, in the case of sudden weather change.

Calculation and application of partition coefficients of light hydrocarbons in oil-based mud system

To find out the relationship between the oil-based mud, the formation fluid and the extracted gas, we use a thermodynamic approach based on static headspace gas chromatography technique to calculate the partition coefficients of 47 kinds of light hydrocarbons compounds between nC5 and nC8 in two kinds of oil-based mud-air systems, and reconstruct the original formation fluid composition under thermodynamic equilibrium. The oil-based drilling mud has little effect on the formation fluid compositions in the range of nC5–nC8 (less than 1% for low-toxicity oil-based mud and less than 10% for oil-based mud). For most light hydrocarbon compositions, the partition coefficients obtained by vapor phase calibration and the direct quantitative methods have errors of less than 10%, and the partition coefficients obtained by direct quantitative method are more accurate. The reconstructed compositions of the two kinds of crude oil have match degrees of 91% and 89% with their real compositions, proving the feasibility and accuracy of reconstructing the composition of original formation fluid by using partition coefficients of light hydrocarbon compositions between nC5 and nC8.

Non-targeted screening and analysis of volatile organic compounds in drinking water by DLLME with GC–MS

Volatile organic compounds (VOCs) in drinking water may potentially be hazardous. We developed a novel non-targeted analysis method of VOCs in drinking water that uses dispersive liquid-liquid microextraction coupled with gas chromatography–mass spectrometry. Analysis parameters were selected from range-finding tests on the peak number and average area of the extracted compounds. The optimized method was applied to analyze VOCs in tap water samples collected from Wuhan City, China. Twenty-seven compounds with high match degrees and a high prevalence were selected for quantification and evaluation. We used structure-activity relationships to predict the carcinogenicity of these compounds. Although most of the compounds were non-toxic, compounds such as dibutyl phthalate and diacetone alcohol should be investigated further. Untargeted analysis of the tap water samples identified 75–200 VOCs, including 67 highly prevalent compounds. Industrial and pharmaceutical chemicals accounted for approximately 70% of the VOCs in the samples. This method of non-targeted analysis and in silico toxicity prediction is simple and economic, and could be used in screening VOCs in drinking water.

Enhanced electrochemical performance of C-NiO/NiCo2O4//AC asymmetric supercapacitor based on material design and device exploration

Asymmetric supercapacitors (ASCs) as a promising candidate to complement batteries, have been studied for decades due to the relatively high power density. The energy storage of ASCs is significantly affected by the well-designed electrode material and delicate charge balance of electrodes. With the hope to design better supercapacitor devices, we firstly synthesize C-NiO/NiCo2O4 nanocomposites with the modified nanowire structure and a small amount of carbon, effectively boosting the specific capacitance and cyclic-stability. In addition, a multi-electrode system is developed to investigate match degrees of C-NiO/NiCo2O4//AC ACSs, and the inter-restricted behavior of positive and negative electrodes. By this real-time electrochemical method, the potential contributions of electrodes in ASCs are further revealed during charge/discharge process. Therefore, ASC device with optimal match degree delivers areal capacitance up to 3482 mF cm−2, and the maximum energy and power density of 42.69 mWh cm−3 and 1.66 W cm−3. Importantly, this work throws light on the fundamental study for supercapacitors in terms of materials design and charge match degrees.

Effective BigData-Space Service Selection over Trust and Heterogeneous QoS Preferences

As the number of Cloud services is growing at a tremendous speed, there is an increasing number of service providers offering similar functionalities. Selecting services with user desired non-functional properties (NFPs) becomes of significant importance but triggers a number of Big Data related research issues. First, the selection decision should deal with a large volume of service NFPs data. Second, service selection needs to reflect diverse user preferences, including both qualitative and quantitative ones. Third, the uncertainty of the network and service load leads to high variability in NFPs. Fourth, as the trust values of service NFPs are collected via historic user's feedbacks,it brings the veracity dimension to the NFPs of services. Fifth, multiple and sometimes conflicting decision objectives for optimal service selection should be balanced. An effective service selection mechanism is in demand that can tackle all the above Big Data challenges in an integrated way to handle the highly diverse QoS with significant variability along with the trust related issues giving rise to data veracity. Existing investigations focus on either users’ QoS preferences or their trust concerns but fail to provide a systematic solution to integrate both criteria in the selection process. In this paper, we tackle heterogeneous preference- and trust-based service selection by developing a novel multi-objective optimization approach to make trade-off decision between service's trust value and user's QoS preference to rank candidate Cloud services based on their match degrees with users’ requirements. We conduct extensive experiments to evaluate the effectiveness and efficiency of the proposed approach.

The Comparison of Efficiency between the Recommendation Algorithm Based on Multi-Attribute Rating Matrix and the Algorithm Based on UIARM

this recommendation algorithm based on User-Item Attribute Rating Matrix (UIARM) can solve the cold-start problem, but the recommended low efficiency, poor quality. The use of Multi-Attribute Rating Matrix (MARM) can solve this problem; it can reduce the computation time and improve the recommendation quality effectively. The user information is analyzed to create their attribute-tables. The user's ratings are mapped to the relevant item attributes and the user's attributes respectively to generate a User Attribute-Item Attribute Rating Matrix. After UAIARM is simplified, MARM will be created. When a new item/user enters into this system, the attributes of new item/user and MARM are matched to find the N users/item with the highest match degrees as the target of the new items or the recommended items. Experiment results validate the cold-start recommendation algorithm based on MARM is efficient.

The Comparison of Efficiency between the Recommendation Algorithm Based on Multi-Attribute Rating Matrix and the Algorithm Based on UIARM

this recommendation algorithm based on User-Item Attribute Rating Matrix (UIARM) can solve the cold-start problem, but the recommended low efficiency, poor quality. The use of Multi-Attribute Rating Matrix (MARM) can solve this problem; it can reduce the computation time and improve the recommendation quality effectively. The user information is analyzed to create their attribute-tables. The user's ratings are mapped to the relevant item attributes and the user's attributes respectively to generate a User Attribute-Item Attribute Rating Matrix. After UAIARM is simplified, MARM will be created. When a new item/user enters into this system, the attributes of new item/user and MARM are matched to find the N users/item with the highest match degrees as the target of the new items or the recommended items. Experiment results validate the cold-start recommendation algorithm based on MARM is efficient.

Left-Handed Wrestlers are More Successful

It has been reported that there is a high proportion of left-handers among top athletes in different sports. In this study, the goal was to examine the rate of left-handedness in the top wrestlers at the world championships for achievement by left-handed wrestlers. The rate of left-handedness was higher in wrestlers with medal honors compared to wrestlers without medals for both men and women. The winning numbers and match degrees were higher in the left-handed wrestlers than in both right- and mixed-handed wrestlers among top international wrestlers.

Efficient subspace clustering for higher dimensional data using fuzzy entropy

In this paper we propose a novel method for identifying relevant subspaces using fuzzy entropy and perform clustering. This measure discriminates the real distribution better by using membership functions for measuring class match degrees. Hence the fuzzy entropy reflects more information in the actual distribution of patterns in the subspaces. We use a heuristic procedure based on the silhouette criterion to find the number of clusters. The presented theories and algorithms are evaluated through experiments on a collection of benchmark data sets. Empirical results have shown its favorable performance in comparison with several other clustering algorithms.