The purpose of this paper is to establish the optimization model and solve the short‐term economic dispatch of cascaded hydro‐plants.
Abstract
Purpose
The purpose of this paper is to establish the optimization model and solve the short‐term economic dispatch of cascaded hydro‐plants.
Design/methodology/approach
An improved particle swarm optimization (IPSO) approach is proposed to solve the short‐term economic dispatch of cascaded hydroelectric plants. The water transport delay time between connected reservoirs is taken into account and it is easy in dealing with the difficult hydraulic and power coupling constraints using the proposed method in practical cascaded hydroelectric plants operation. The feasibility of the proposed method is demonstrated for actual cascaded hydroelectric plant.
Findings
The simulation results show that this approach can prevent premature convergence to a high degree and keep a rapid convergence speed.
Research limitations/implications
The optimal values of parameters in the proposed method are the main limitations where the method will be applied to the economic operation of the hydro‐plant.
Practical implications
The paper presents useful advice for short‐term economic operations of the hydro‐plant. A new optimization method to solve the short‐term optimal generation scheduling is proposed. The optimal generation power and water discharge during the whole dispatching time for hydro‐plant operation can be obtained.
Originality/value
The IPSO method is realized by maintaining high diversity of the swarm during the optimization process and preventing premature convergence.
Details
Keywords
Xuying Li, Yanbin Liu, Jie Huang, Deyu Sang, Kun Yang and Jinbo Ling
This paper aims to reveal the influence of the grooved texture parameters on the lubrication performance of circular pocket-roller pairs in cylindrical roller bearings.
Abstract
Purpose
This paper aims to reveal the influence of the grooved texture parameters on the lubrication performance of circular pocket-roller pairs in cylindrical roller bearings.
Design/methodology/approach
In this paper, the thermal elastohydrodynamic lubrication mathematical model of the grooved texture circular pocket-roller pair was established, the finite difference method and successive over-relaxation method were used to solve the model, the influence of texture quantity, texture depth and texture area ratio on circumferential bearing capacity, friction coefficient, maximum temperature rise, stiffness and damping of the circular pocket-roller pairs were analyzed.
Findings
The results show that texture quantity, texture depth and texture area ratio significantly influence the static and dynamic characteristics of circular pocket-roller pairs. The suitable surface groove texture parameters can dramatically improve the circumferential bearing capacity, reduce the friction coefficient, inhibit the maximum temperature rise and increase the stiffness and damping of the circular pocket-roller pairs.
Originality/value
The research in this paper can provide a theoretical basis for the optimization design of pockets in cylindrical roller bearings to reduce friction and vibration.
Details
Keywords
Zhenghao Liu, Yuxing Qian, Wenlong Lv, Yanbin Fang and Shenglan Liu
Stock prices are subject to the influence of news and social media, and a discernible co-movement pattern exists among multiple stocks. Using a knowledge graph to represent news…
Abstract
Purpose
Stock prices are subject to the influence of news and social media, and a discernible co-movement pattern exists among multiple stocks. Using a knowledge graph to represent news semantics and establish connections between stocks is deemed essential and viable.
Design/methodology/approach
This study presents a knowledge-driven framework for predicting stock prices. The framework integrates relevant stocks with the semantic and emotional characteristics of textual data. The authors construct a stock knowledge graph (SKG) to extract pertinent stock information and use a knowledge graph representation model to capture both the relevant stock features and the semantic features of news articles. Additionally, the authors consider the emotional characteristics of news and investor comments, drawing insights from behavioral finance theory. The authors examined the effectiveness of these features using the combined deep learning model CNN+LSTM+Attention.
Findings
Experimental results demonstrate that the knowledge-driven combined feature model exhibits significantly improved predictive accuracy compared to single-feature models.
Originality/value
The study highlights the value of the SKG in uncovering potential correlations among stocks. Moreover, the knowledge-driven multi-feature fusion stock forecasting model enhances the prediction of stock trends for well-known enterprises, providing valuable guidance for investor decision-making.
Details
Keywords
Xiaohua Bao, Guanlin Ye, Bin Ye, Yanbin Fu and Dong Su
The purpose of this paper is to evaluate the co-seismic and post-seismic behaviors of an existed soil-foundation system in an actual alternately layered sand/silt ground including…
Abstract
Purpose
The purpose of this paper is to evaluate the co-seismic and post-seismic behaviors of an existed soil-foundation system in an actual alternately layered sand/silt ground including pore water pressure, acceleration response, and displacement et al. during and after earthquake.
Design/methodology/approach
The evaluation is performed by finite element method and the simulation is performed using an effective stress-based 2D/3D soil-water coupling program DBLEAVES. The calculation is carried out through static-dynamic-static three steps. The soil behavior is described by a new rotational kinematic hardening elasto-plastic cyclic mobility constitutive model, while the footing and foundation are modeled as elastic rigid elements.
Findings
The shallow (short-pile type) foundation has a better capacity of resisting ground liquefaction but large differential settlement occurred. Moreover, most part of the differential settlement occurred during earthquake motion. Attention should be paid not only to the liquefaction behavior of the ground during the earthquake motion, but also the long-term settlement after earthquake should be given serious consideration.
Originality/value
The co-seismic and post-seismic behavior of a complex ground which contains sand and silt layers, especially long-term settlement over a period of several weeks or even years after the earthquake, has been clarified sufficiently. In some critical condition, even if the seismic resistance is satisfied with the design code for building, detailed calculation may reveal the risk of under estimation of differential settlement that may give rise to serious problems.
Details
Keywords
Abstract
Purpose
Previous studies focused on the influence of outsourcing (labor division) on productivity, especially in the industrial economy. However, few studies have focused on how labor division in agriculture affects agricultural productivity. To bridge this gap, this study uses survey data from 4864 farmer households in China to explore the impacts of outsourcing on agricultural productivity.
Design/methodology/approach
This study employs an endogenous switching regression to account for selection bias and a counterfactual framework to measure the degree of influence. Thus, this study analyzes determinants of outsourcing and the impacts of outsourcing on agricultural productivity under the same framework.
Findings
The results revealed the following. (1) Farmer households with the below average productivity tended to outsource; conversely, farmer households with the above average productivity tended to cultivate the land by themselves. (2) Productivity increased by 25.61% for farmer households who choose to outsource. Moreover, if nonoutsourcing farmer households would choose to outsource, their productivity would increase by 10.86%.
Originality/value
This study furthers one’s understanding of how outsourcing affects agricultural productivity among farmer households.
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The purpose of this guest editorial is to introduce the papers in this special issue.
Abstract
Purpose
The purpose of this guest editorial is to introduce the papers in this special issue.
Design/methodology/approach
A brief introduction about the issue of web‐mining applications in e‐commerce and e‐services is provided, along with a summary of the main contributions of the papers that are included in the special issue.
Findings
The value of web‐mining techniques can be enhanced through applying them to real environments such as e‐commerce and e‐services. The research fields of web mining, e‐commerce and e‐services can also be expanded.
Originality/value
An overview of the special issue and related research is provided in this paper.
Details
Keywords
Facial expression provides abundant information for social interaction, and the analysis and utilization of facial expression data are playing a huge driving role in all areas of…
Abstract
Purpose
Facial expression provides abundant information for social interaction, and the analysis and utilization of facial expression data are playing a huge driving role in all areas of society. Facial expression data can reflect people's mental state. In health care, the analysis and processing of facial expression data can promote the improvement of people's health. This paper introduces several important public facial expression databases and describes the process of facial expression recognition. The standard facial expression database FER2013 and CK+ were used as the main training samples. At the same time, the facial expression image data of 16 Chinese children were collected as supplementary samples. With the help of VGG19 and Resnet18 algorithm models of deep convolution neural network, this paper studies and develops an information system for the diagnosis of autism by facial expression data.
Design/methodology/approach
The facial expression data of the training samples are based on the standard expression database FER2013 and CK+. FER2013 and CK+ databases are a common facial expression data set, which is suitable for the research of facial expression recognition. On the basis of FER2013 and CK+ facial expression database, this paper uses the machine learning model support vector machine (SVM) and deep convolution neural network model CNN, VGG19 and Resnet18 to complete the facial expression recognition.
Findings
In this study, ten normal children and ten autistic patients were recruited to test the accuracy of the information system and the diagnostic effect of autism. After testing, the accuracy rate of facial expression recognition is 81.4 percent. This information system can easily identify autistic children. The feasibility of recognizing autism through facial expression is verified.
Research limitations/implications
The CK+ facial expression database contains some adult facial expression images. In order to improve the accuracy of facial expression recognition for children, more facial expression data of children will be collected as training samples. Therefore, the recognition rate of the information system will be further improved.
Originality/value
This research uses facial expression data and the latest artificial intelligence technology, which is advanced in technology. The diagnostic accuracy of autism is higher than that of traditional systems, so this study is innovative. Research topics come from the actual needs of doctors, and the contents and methods of research have been discussed with doctors many times. The system can diagnose autism as early as possible, promote the early treatment and rehabilitation of patients, and then reduce the economic and mental burden of patients. Therefore, this information system has good social benefits and application value.