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The preparation and performance of a Ti/IrO2–RuO2–TiO2 anode in the removal of chemical oxygen demand (COD) from biochemically treated landfill leachate by the Fered-Fenton process were investigated. The Taguchi design was applied to obtain the optimal conditions for preparation of the Ti/IrO2–RuO2–TiO2 anode by thermal decomposition method. The optimal preparation conditions were as follows: Ir and...
The paper focuses on short-term forecasting of air pollutants including SO2, NO2, O3 and PM2.5. A hybrid model of nonlinear autoregressive with exogenous input (NARX) network and autoregressive moving average (ARMA) is applied. The NARX network is used to solve the problem of nonlinear and multidimensional while the ARMA model is aimed to improve the flexibility for different pollutants. The performance...
Electrical load forecasting is vitally important to modern power system planning, operation, and control. In this paper, by focusing on historical load data and calendar factors, we present a hybrid method using period refinement scheme and adaptive strategy for building peak hour period and off-peak hour period models in day-of-week for one-day-ahead for load forecasting. They are evaluated using...
Prognosis of the remaining battery life is an important and practical research area of rechargeable battery and smart grid. It has promising application prospect in such area as grid energy storage systems, electrical vehicles etc. In this paper, by analysing the lithium-ion battery information, the most influencing factors of lifetime are collected. Based on this, a novel system is proposed to predict...
This paper presents a new type of linear regression model called sparse linear regression (SLR) model for short-term wind speed forecasting. Modifications are applied to the SLR model and some other variant models are proposed. Experiments are carried out on real wind farm history recording data. Results show SLR model and its variants can improve the accuracy of the short-term forecasting result...
Wind power is an increasingly used form of renewable energy. However, the inherent randomicity and intermittency of wind resource brings challenges to operators of power systems and wind farms. Therefore, preliminary forecasting of the wind power is necessary. We propose a statistical method for it based on linear regression model. Moreover, many of farms need to be worked on at the same time in some...
Nowadays, choosing potential cities where to open new stores (especially supermarkets) and expand business plays a very important role for leading retailers due to the influence of huge and long-term investment. However, it is very time consuming for decision makers or experienced analysts to go through the whole city data and determine which city is better for a new supermarket. In the computer aided...
Collaborative filtering (CF) methods are popular for recommender systems. In this paper we focus on exploring how to use implicit and hybrid information to produce efficient recommendations. We suggest a new similarity measure and rating strategy for neighborhood models, and extend original matrix factorization (MF) models to explore implicit information more efficiently. By the mean time, We extend...
As wind has the property of intermittency and randomness, it is very important for the wind plant to improve the accuracy for wind power prediction to satisfy the stability requirements when combined to the grid. This paper studies the major factors that influence wind power, and proposes a BP neural network model based on cluster analysis with the traditional BP neural network model for comparison...
In this paper, we propose a novel probabilistic graphical model to address the off-line signature verification problem. Different from previous work, our approach introduces the concept of feature roles according to their distribution in genuine and forgery signatures, with all these features represented by a unique graphical model. And we propose several new techniques to improve the performance...
For a customer-based supermarket, usually two types of data are available. One is demographic data, and the other is transactional data. Most of previous researchers and practitioners mainly focus on solely utilizing either type of data to segment customers. To obtain a stable and implementable consumer segmentation methodology with high efficiency and product recommendation accuracy for targeted...
EAI and SOA are widely adopted in enterprise information systems with business processes being orchestrated by a process engine. An algorithm which schedules tasks of process instances to maximize overall customer satisfaction is proposed in this paper. This new algorithm maximizes the total value of all process instances by dynamically assigning different priority to each task based on the business...
We present an innovative approach for clustering retail customers using semi-supervised geographic information. The approach aims at clustering (or segmenting) customers not only depending on their age, spending, etc., but also on their dwelling, which can discover useful customer patterns for the retailer's marketing strategy. In real retail applications, unsupervised clustering faces the problem...
With the increasingly intense competition, China's supermarket retailers call for a complete, stable and practical customer segmentation approach to utilize consumer data so as to effectively customize the firm's marketing efforts and targets. We propose a novel three-step segmentation framework to reveal the consumer's demographics, behavioral, psychological characteristics in supermarket shopping,...
How best to locate new outlets among alternative position offerings is a critical operational decision that is faced by all retailers. Thus they are eager to have a quantitative method and tool to help them derive more scientific strategic suggestions. In this paper, the retail site location (RSL) problem is described at the beginning. Then we propose a two-stage guiding optimization procedure to...
We address the problem of feature weight learning for image clustering. In practice, before clustering data, we generally normalize all data features between 0 and 1, because we cannot determine which features are more important. In this paper, we provide a feature weight learning framework for clustering which can obtain the feature weights and cluster labels simultaneously. An alternative optimization...
With the development of online commerce, its becoming more and more important to deal with multi-channel problem, especially when there is an e-channel. In this paper, we establish neural networks to describe customers' decision process. Based on the neural networks, a new price setting game is proposed, which can simulate the competition in multichannel. Since the networks can reflect the real situation...
Merchandise hierarchy plays an important role in modern retail business. In this paper, we suggest a quantitative consumer-related evaluation method of merchandise hierarchy via clustering the retail records. The retail records contains much information reflecting the consumer buying behavior, and it should be effectively utilized to judge whether a predefined merchandise hierarchy is appropriate...
Facility is the most important but costly channel for many enterprises to win in the customer-centric marketplace. How to optimize facility site network is part of the critical strategic nuts for executives. Many enterprises have adopted geographic information system (GIS) based solutions to support facility related business analysis, which, however, are usually based on desktop applications and are...
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