Search results

The purpose of this study is to propose a new consumer value segmentation method for low-dimensional dense market datasets to quickly detect and cluster the most profitable customers for the enterprises.

In this study, the comprehensive segmentation bases (CSB) with richer meanings were obtained by introducing the weighted recency-frequency-monetary (RFM) model into the common segmentation bases (SB). Further, a new market segmentation method, the CSB-MBK algorithm was proposed by integrating the CSB model and the mini-batch k-means (MBK) clustering algorithm.

The results show that our proposed CSB model can reflect consumers' contributions to a market, as well as improve the clustering performance. Moreover, the proposed CSB-MBK algorithm is demonstrably superior to the SB-MBK, CSB-KMA and CSB-Chameleon algorithms with respect to the Silhouette Coefficient (SC), the Calinski-Harabasz (CH) Index , the average running time and superior to the SB-MBK, RFM-MBK and WRFM-MBK algorithms in terms of the inter-market value and characteristic differentiation.

This paper provides a tool for decision-makers and marketers to segment a market quickly, which can help them grasp consumers' activity, loyalty, purchasing power and other characteristics in a target market timely and achieve the precision marketing.

This study is the first to introduce the CSB-MBK algorithm for identifying valuable customers through the comprehensive consideration of the clustering quality, consumer value and segmentation speed. Moreover, the CSB-MBK algorithm can be considered for applications in other markets.

The purpose of this paper is to propose a novel Unmanned Combat Air Vehicle (UCAV) flight controller parameters identification method, which is based on predator-prey Biogeography-Based Optimization (PPBBO) algorithm, with the objective of optimizing the whole UCAV system design process.

The hybrid model of predator-prey theory and biogeography-based optimization (BBO) algorithm is established for parameters identification of UCAV. This proposed method identifies controller parameters and reduces the computational complexity.

The basic BBO is improved by modifying the search strategy and adding some limits, so that it can be better applied to the parameters identification problem. Comparative experimental results demonstrated the feasibility and effectiveness of the proposed method: it can guarantee finding the optimal controller parameters, with the rapid convergence.

The proposed PPBBO algorithm can be easily applied to practice and can help the design of the UCAV flight control system, which will considerably increase the autonomy of the UCAV.

A hybrid model of predator-prey theory and BBO algorithm is proposed for parameters identification of UCAV, and a PPBBO-based software platform for UCAV controller design is also developed.

The purpose of this paper is to provide a review of the issues related to cluster analysis, one of the most important and primitive activities of human beings, and of the advances made in recent years.

The paper investigates the clustering algorithms rooted in machine learning, computer science, statistics, and computational intelligence.

The paper reviews the basic issues of cluster analysis and discusses the recent advances of clustering algorithms in scalability, robustness, visualization, irregular cluster shape detection, and so on.

The paper presents a comprehensive and systematic survey of cluster analysis and emphasizes its recent efforts in order to meet the challenges caused by the glut of complicated data from a wide variety of communities.

The purpose of this paper is to introduce the notion of a social fabric (SF) in which the expression of knowledge sources (KS) in cultural algorithms (CA) can be distributed through the population. The SF influence function is applied to the solution of selected complex engineering problems and it is shown that different parameter combinations for the SF influence function can affect the rate of solution. This enhanced approach is compared with previous approaches.

KS are allowed to influence individuals through a network. From a theoretical perspective, individuals in the real world are viewed as participating in a variety of different networks. Several layers of such networks can be supported within a population. The interplay of these various network computations is designated as the “social fabric.” Using this new influence function, when an individual is to be modified, one KS is selected to perform the modification at each generation. The selection process is done via weaving the SF, hence changing the number of individuals that follow a certain KS.

Simulation experiments show that the choice of influence function has a great impact on the problem‐solving phase. For some problems, a social network is not necessary to produce frequent convergence to an optimum. On the other hand, it is observed that the social network can help to focus search by allowing a KS to influence groups of individuals within a network rather than single unrelated individuals. The new approach shows a more focused convergence to optimal values in complex engineering problems with numerous constraints. Also, it is suggested that a SF configuration can be robust in the sense that a configuration that works well for one problem can also perform well in a more complex but unrelated problem. This suggests that a configuration can be evolved to solve suites of problems.

The introduced approach is interesting for the optimization of problems of a non‐linear complex nature.

The purpose of this paper is to use the viscosity of fluid to transmit power known as the hydro-viscous drive (HVD) to research the effect of two-phase flow on transmission characteristics.

In this paper, a 3D computational model of oil film between friction pair was built to study the transmission characteristics of a two-phase oil film, and the distribution contours of pressure and temperature of oil film were investigated using the computational fluid dynamics technology.

The finding of the paper suggests that the distribution law of pressure and temperature of two-phase oil film is almost linear along the radial direction. However, since the physical phenomena near the outlet of the oil film are entirely different, there exists fluctuation. Meanwhile, the volume fraction of air was obtained at different rotation speeds, and the maximum value is 10.55 percent. Compared to the single-phase oil film, the torque transferred by the oil film is not linear with the rotation speed, its value decreases gradually.

This paper’s conclusions are very important for the study of HVD and its applications, which provide a new idea to further study the mechanism of oil film transmission and its cavitation. The development of fluid viscous speed clutch is dedicated to a large industrial fan and water pump speed regulation and energy saving. With the successful application of the technology, it will have more wide applications in different fields, such as, in steel, water, petrochemical, power plant of slag pump and exhaust fan.

The purpose of this paper is to propose a new algorithm for linear-quadratic regulator (LQR) controller of a quadrotor with fast and stable performance, which is based on pigeon-inspired optimization (PIO).

The controller is based on LQR. The determinate parameters are optimized by PIO, which is a newly proposed swarm intelligent algorithm inspired by the characteristics of homing pigeons.

The PIO-optimized LQR controller can obtain the optimized parameters and achieve stabilization in about 3 s.

The PIO-optimized LQR controller can be easily applied to the flight formation, autonomous aerial refueling (AAR) and detection of unmanned aerial vehicles, especially applied to (AAR) in this paper.

This research applies PIO to optimize the tuning parameters of LQR, which can considerably improve the fast and stabilizing performance of attitude control. The simulation results show the effectiveness of the proposed algorithm.

The purpose of this paper is to propose a new algorithm for pendulum‐like oscillation control of an unmanned rotorcraft (UR) in a reconnaissance mission and improve the stabilizing performance of the UR's hover and stare.

The algorithm is based on linear‐quadratic regulator (LQR), of which the determinable parameters are optimized by the artificial bee colony (ABC) algorithm, a newly developed algorithm inspired by swarm intelligence and motivated by the intelligent behaviour of honey bees.

The proposed algorithm is tested in a UR simulation environment and achieves stabilization of the pendulum oscillation in less than 4s.

The presented algorithm and design strategy can be extended for other types of complex control missions where relative parameters must be optimized to get a better control performance.

The ABC optimized control system developed can be easily applied to practice and can safely stabilize the UR during hover and stare, which will considerably improve the stability of the UR and lead to better reconnaissance performance.

This research presents a new algorithm to control the pendulum‐like oscillation of URs, whose performance of hover and stare is a key issue when carrying out new challenging reconnaissance missions in urban warfare. Simulation results show that the presented algorithm performs better than traditional methods and the design process is simpler and easier.

Unmanned aerial/ground vehicles (UAV/UGV) collaboration systems are increasingly being used to perform reconnaissance and rescue missions autonomously, especially in disaster areas. The paper aims to discuss this issue.

To improve visibility, this study proposes a path-planning algorithm based on map matching. Continuous ground images are first collected aerially using the UAV vision system. Subsequently, a global map of the ground environment is created by processing the collected images using the methods of image correction, image mosaic and obstacle recognition. The local map of the ground environment is obtained using the 2D laser radar sensor of the UGV. A set of features for both global and local maps is established. Unknown values during map matching are determined via the least squares method. Based on the matched mapping, the traditional A* algorithm is used for the planning of global path in the global map, and the dynamic window method is used for adjustment of the local map.

Simulation experiments were carried out to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm can construct a global map of the wide environment and effectively bypass the obstacles missed by the UAV.

Prior to map matching, there is a need to extract the edge of obstacles in the global map.

This paper proposed a path planning algorithm based on map matching, yielding insights into the application of the UAV/UGV collaboration systems in disaster areas.

The purpose of this paper is to systematically analyze the literature around regulatory compliance and market manipulation in capital markets through the use of bibliometrics and propose future research directions. Under the domain of capital markets, this theme is a niche area of research where greater academic investigations are required. Most of the research is fragmented and limited to a few conventional aspects only. To address this gap, this study engages in a large-scale systematic literature review approach to collect and analyze the research corpus in the post-2000 era.

The big data corpus comprising research articles has been extracted from the scientific Scopus database and analyzed using the VoSviewer application. The literature around the subject has been presented using bibliometrics to give useful insights on the most popular research work and articles, top contributing journals, authors, institutions and countries leading to identification of gaps and potential research areas.

Based on the review, this study concludes that, even in an era of global market integration and disruptive technological advancements, many important aspects of this subject remain significantly underexplored. Over the past two decades, research has lagged behind the evolution of capital market crime and market regulations. Finally, based on the findings, the study suggests important future research directions as well as a few research questions. This includes market manipulation, market regulations and new-age technologies, all of which could be very useful to researchers in this field and generate key inputs for stock market regulators.

The limitation of this research is that it is based on Scopus database so the possibility of omission of some literature cannot be completely ruled out. More advanced machine learning techniques could be applied to decode the finer aspects of the studies undertaken so far.

Increased integration among global markets, fast-paced technological disruptions and complexity of financial crimes in stock markets have put immense pressure on market regulators. As economies and equity markets evolve, good research investigations can aid in a better understanding of market manipulation and regulatory compliance. The proposed research directions will be very useful to researchers in this field as well as generate key inputs for stock market regulators to deal with market misbehavior.

This study has adopted a period-wise broad-based scientific approach to identify some of the most pertinent gaps in the subject and has proposed practical areas of study to strengthen the literature in the said field.