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1 – 10 of over 43000Saad Ahmed Javed, Muhammad Ikram, Liangyan Tao and Sifeng Liu
Tourism industry is a highly complex system surrounded by many uncertainties because of its innumerable connections with other supporting systems. Considering tourism, a grey…
Abstract
Purpose
Tourism industry is a highly complex system surrounded by many uncertainties because of its innumerable connections with other supporting systems. Considering tourism, a grey system, the current study proposes optimistic–pessimistic method (OPM). This technique can aid in improving forecast accuracy of four tourism-related indicators, inbound tourism to China, outbound tourism from China, revenues collected through inbound tourism and expenses incurred on outbound tourism.
Design/methodology/approach
The study integrates OPM into EGM and then using the secondary data collected from the World Bank database, predicts the four tourism-related indicators. The mean absolute percentage error steered the performance of the models.
Findings
One of the main contributions of the study lies in its overall evaluation of one of the major travel and tourism countries of the world in light of four crucial indicators. The study highlights, four tourism-related indicators' recent information, contains more valuable information about the future.
Originality/value
OPM represents a novel application of concept of whitenization of interval grey number in grey forecasting theory.
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Zhixin Wang, Peng Xu, Bohan Liu, Yankun Cao, Zhi Liu and Zhaojun Liu
This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some…
Abstract
Purpose
This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed.
Design/methodology/approach
First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions.
Findings
Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future.
Originality/value
This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.
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Weiliang Zhang, Sifeng Liu, Lianyi Liu, R.M. Kapila Tharanga Rathnayaka, Naiming Xie and Junliang Du
China's population aging is gradually deepening and needs to be actively addressed. The purpose of this paper is to construct a novel model for analyzing the population aging.
Abstract
Purpose
China's population aging is gradually deepening and needs to be actively addressed. The purpose of this paper is to construct a novel model for analyzing the population aging.
Design/methodology/approach
To analyze the aging status of a region, this study has considered three major indicators: total population, aged population and the proportion of the aged population. Additionally, the authors have developed a novel grey population prediction model that incorporates the fractional-order accumulation operator and Gompertz model (GM). By combining these techniques, the authors' model provides a comprehensive and accurate prediction of population aging trends in Jiangsu Province. This research methodology has the potential to contribute to the development of effective policy solutions to address the challenges posed by the population aging.
Findings
The fractional-order discrete grey GM is suitable for predicting the aging population and has good performance. The population aging of Jiangsu Province will continue to deepen in the next few years.
Practical implications
The proposed model can be used to predict and analyze aging differences in Jiangsu Province. Based on the prediction and analysis results, identified some corresponding countermeasures are suggested to address the challenges of Jiangsu's future aging problem.
Originality/value
The fractional-order discrete grey GM is firstly proposed in this paper and this model is a novel grey population prediction model with good performance.
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Shuai Yue, Ben Niu, Huanqing Wang, Liang Zhang and Adil M. Ahmad
This paper aims to study the issues of adaptive fuzzy control for a category of switched under-actuated systems with input nonlinearities and external disturbances.
Abstract
Purpose
This paper aims to study the issues of adaptive fuzzy control for a category of switched under-actuated systems with input nonlinearities and external disturbances.
Design/methodology/approach
A control scheme based on sliding mode surface with a hierarchical structure is introduced to enhance the responsiveness and robustness of the studied systems. An equivalent control and switching control rules are co-designed in a hierarchical sliding mode control (HSMC) framework to ensure that the system state reaches a given sliding surface and remains sliding on the surface, finally stabilizing at the equilibrium point. Besides, the input nonlinearities consist of non-symmetric saturation and dead-zone, which are estimated by an unknown bounded function and a known affine function.
Findings
Based on fuzzy logic systems and the hierarchical sliding mode control method, an adaptive fuzzy control method for uncertain switched under-actuated systems is put forward.
Originality/value
The “cause and effect” problems often existing in conventional backstepping designs can be prevented. Furthermore, the presented adaptive laws can eliminate the influence of external disturbances and approximation errors. Besides, in contrast to arbitrary switching strategies, the authors consider a switching rule with average dwell time, which resolves control problems that cannot be resolved with arbitrary switching signals and reduces conservatism.
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The purpose of this paper is to develop some picture uncertain linguistic aggregation operators based on Bonferroni mean operators, which is combined with multiple attribute…
Abstract
Purpose
The purpose of this paper is to develop some picture uncertain linguistic aggregation operators based on Bonferroni mean operators, which is combined with multiple attribute decision-making (MADM) and has applied the proposed MADM model for selecting the service outsourcing provider of communications industry under picture uncertain linguistic environment.
Design/methodology/approach
The service outsourcing provider selection problem of communications industry can be regarded as a typical MADM problem, in which the decision information should be aggregated. In this paper, the authors investigate the MADM problems with picture uncertain linguistic information based on traditional Bonferroni mean operator.
Findings
The results show that the proposed model can solve the MADM problems within the context of picture uncertain linguistic information, in which the attributes are existing interaction phenomenon. Some picture uncertain aggregation operators based on Bonferroni mean have been developed. A case study of service outsourcing provider selection problem of communications industry is provided to illustrate the effectiveness and feasibility of the proposed methods. The results show that the proposed methods are useful to aggregate the picture uncertain linguistic decision information in which the attributes are not independent so as to select the most suitable supplier.
Research limitations/implications
The proposed methods can solve the picture uncertain linguistic MADM problem, in which the interactions exist among the attributes. Therefore, it can be used to solve service outsourcing provider selection problems and other similar management decision problems.
Practical implications
This paper develops some picture uncertain aggregation operators based on Bonferroni mean and further presents two methods based on the proposed operators for solving MADM problems. It is useful to deal with multiple attribute interaction decision-making problems and suitable to solve a variety of management decision-making applications.
Social implications
It is useful to deal with multiple attribute interaction decision-making problems and suitable to solve a variety of management decision-making applications.
Originality/value
The paper investigates the MADM problems with picture uncertain linguistic information based on traditional Bonferroni mean operator and develops the picture uncertain linguistic Bonferroni mean operator and picture uncertain linguistic geometric Bonferroni mean operator, picture uncertain linguistic weighted Bonferroni mean operator and picture uncertain linguistic weighted geometric Bonferroni mean operator for aggregating the picture uncertain linguistic information, respectively. Finally, a numerical example concerning the service outsourcing provider selection problem of communications industry is provided to illustrate the effectiveness and feasibility of the proposed methods.
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Keywords
Dang Luo, Haitao Li and Qicun Qian
The purpose of this paper is to construct a key factors selection approach for a class of small-sample multi-factor cross-sectional data analysis (SMCDA) problem, which is very…
Abstract
Purpose
The purpose of this paper is to construct a key factors selection approach for a class of small-sample multi-factor cross-sectional data analysis (SMCDA) problem, which is very common in productive practice and scientific research, such as coal-bed methane (CBM) content analysis, civil aircraft cost analysis, etc. Key factors selection is an important basic work for SMCDA problem; the proposed method is constructed to improve the accuracy and explanatory of the selected key factors.
Design/methodology/approach
Using grey system theory to solve SMCDA problem is more reasonable under few data and poor information. Therefore, this paper constructs a grey incidence analysis (GIA) model with rate of change to select the key factors of an SMCDA problem. The basic idea of the proposed method is to simulate time series by randomly sorting the selected samples, and to calculate the degree of grey incidence with rate of change by loop iterative algorithm, then to construct the degree matrix of grey incidence with rate of change, and finally by which, to utilise quantitative and qualitative analysis methods to select the key factors.
Findings
The experimental analysis of application cases demonstrates that the key factors of system’s characteristic can be successfully screened out by the proposed method, the results are consistent with actual conditions, and they have a clearer meaning and a better interpretability.
Practical implications
The method proposed in this paper could be utilised to select key factors for such a class of SMCDA problem, which has fewer observation samples (small-sample), which is influenced by a number of factors (multi-factor) and whose observation samples are placed randomly rather than by time (cross-sectional data). Taking the key influence factors of CBM content and the key driving factors of the vulnerability of agricultural drought in Henan as examples, the results proved the feasibility and superiority of this proposed method.
Originality/value
Most of the existing GIA models mainly focus on these classes of issues with time series data or panel data. However, few GIA models take SMCDA problem as the research object. In this paper, the authors develop the GIA model with rate of change according to the characteristics of SMCDA problem, and present some properties and application suggestions of the proposed method.
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Qiang Li, Sifeng Liu and Changhai Lin
The purpose of this paper is to solve the problem of quality prediction in the equipment production process and provide a method to deal with abnormal data and solve the problem…
Abstract
Purpose
The purpose of this paper is to solve the problem of quality prediction in the equipment production process and provide a method to deal with abnormal data and solve the problem of data fluctuation.
Design/methodology/approach
The analytic hierarchy process-process failure mode and effect analysis (AHP-PFMEA) structure tree is established based on the analytic hierarchy process (AHP) and process failure mode and effect analysis (PFMEA). Through the failure mode analysis table of the production process, the weight of the failure process and stations is determined, and the ranking of risk failure stations is obtained so as to find out the serious failure process and stations. The spectrum analysis method is used to identify the fault data and judge the “abnormal” value in the fault data. Based on the analysis of the impact, an “offset operator” is designed to eliminate the impact. A new moving average denoise operator is constructed to eliminate the “noise” in the original random fluctuation data. Then, DGM (1,1) model is constructed to predict the production process quality.
Findings
It is discovered the “offset operator” can eliminate the impact of specific shocks effectively, moving average denoise operator can eliminate the “noise” in the original random fluctuation data and the practical application of the shown model is very effective for quality predicting in the equipment production process.
Practical implications
The proposed approach can help provide a good guidance and reference for enterprises to strengthen onsite equipment management and product quality management. The application on a real-world case showed that the DGM (1,1) grey discrete model is very effective for quality predicting in the equipment production process.
Originality/value
The offset operators, including an offset operator for a multiplicative effect and an offset operator for an additive effect, are proposed to eliminate the impact of specific shocks, and a new moving average denoise operator is constructed to eliminate the “noise” in the original random fluctuation data. Both the concepts of offset operator and denoise operator with their calculation formulas were first proposed in this paper.
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Keywords
Lin Feng, Yang Liu, Zan Li, Meng Zhang, Feilong Wang and Shenglan Liu
The purpose of this paper is to promote the efficiency of RGB-depth (RGB-D)-based object recognition in robot vision and find discriminative binary representations for RGB-D based…
Abstract
Purpose
The purpose of this paper is to promote the efficiency of RGB-depth (RGB-D)-based object recognition in robot vision and find discriminative binary representations for RGB-D based objects.
Design/methodology/approach
To promote the efficiency of RGB-D-based object recognition in robot vision, this paper applies hashing methods to RGB-D-based object recognition by utilizing the approximate nearest neighbors (ANN) to vote for the final result. To improve the object recognition accuracy in robot vision, an “Encoding+Selection” binary representation generation pattern is proposed. “Encoding+Selection” pattern can generate more discriminative binary representations for RGB-D-based objects. Moreover, label information is utilized to enhance the discrimination of each bit, which guarantees that the most discriminative bits can be selected.
Findings
The experiment results validate that the ANN-based voting recognition method is more efficient and effective compared to traditional recognition method in RGB-D-based object recognition for robot vision. Moreover, the effectiveness of the proposed bit selection method is also validated to be effective.
Originality/value
Hashing learning is applied to RGB-D-based object recognition, which significantly promotes the recognition efficiency for robot vision while maintaining high recognition accuracy. Besides, the “Encoding+Selection” pattern is utilized in the process of binary encoding, which effectively enhances the discrimination of binary representations for objects.
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Karl Marx's social capital reproduction theory is his significant contribution to economics. The purpose of this paper is to review the contributions of the exploration of Chinese…
Abstract
Purpose
Karl Marx's social capital reproduction theory is his significant contribution to economics. The purpose of this paper is to review the contributions of the exploration of Chinese economists (especially Professor Liu Guoguang) in the concretization of Marx’s social capital reproduction theory combined with socialist construction since 1949.
Design/methodology/approach
During this process, Professor Liu Guoguang, a famous Chinese Marxist economist, has made an outstanding contribution by creating a Marxist social capital reproduction model with Chinese characteristics and a distinctive Marxist economic growth model. Professor Liu's exploration is still of crucial practical significance to building a socialist market economy today.
Findings
The process and achievements in the sinicization exploration of Marx's social capital reproduction theory were reviewed. With the reform and opening up, fundamental changes have occurred in China's economic system – the centralized planned economic system has been transformed into a socialist market economic system.
Originality/value
The planned management of the national economy is replaced by a macro-regulation system characterized by gross control gradually, and the concepts of agriculture, light industry, and heavy industry, and their intercorrelation are no longer applied in theory and policy. However, the sinicization exploration of Marx's social capital reproduction theory in the older generation of Marxist economists represented by Liu is not only of historical significance but also of important practical significance.
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Ruilin Guo, Qiufang Wu, Yafei Liu and Yanzhen Liu
The purpose of this paper is to attempt to solve a decision‐making problem for breeding target character showed as an interval number.
Abstract
Purpose
The purpose of this paper is to attempt to solve a decision‐making problem for breeding target character showed as an interval number.
Design/methodology/approach
A new comprehensive evaluation method was proposed based on similarity‐difference theory and interval number theory. The data from Winter Wheat Group I Variety Regional Test in Henan Province in 2009‐2010 were analysed using the proposed method.
Findings
The results showed that Zhou 99233 was a good variety, Yuxhan No.7, An 05‐28, Xun K8, Jinyumai 378 and Zhoumai 18 were better ones, 08 luo 36, and Xuke 718 ordinary ones, and others worse ones. Based on this, the feasibility of the method was discussed. It showed that the proposed method had some obvious merits, such as arithmetic was simple, operation convenient, flexible and practical, fast and effective, etc.
Practical implications
Application to a living example indicated that its evaluation effect was satisfactory. Consequently, the application prospect of the method will be very vast.
Originality/value
The paper succeeds in solving a decision‐making problem for breeding target character shown as an interval number.
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