Recommender systems are used to make recommendations about products, information, or services for users. Most existing recommender systems implicitly assume one particular type of user behavior. However, they seldom c...
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Recommender systems are used to make recommendations about products, information, or services for users. Most existing recommender systems implicitly assume one particular type of user behavior. However, they seldom consider user-recommender interactive scenarios in real-world environments. In this paper, we propose a hybrid recommender system based on user-recommender interaction and evaluate its performance with recall and diversity metrics. First, we define the user-recommender interaction. The recommender system accepts user request, recommends N items to the user, and records user choice. If some of these items favor the user, she will select one to browse and continue to use recommender system, until none of the recommended items favors her. Second, we propose a hybrid recommender system combining random and k-nearest neighbor algorithms. Third, we redefine the recall and diversity metrics based on the new scenario to evaluate the recommender system. Experiments results on the well-known MovieLens dataset show that the hybrid algorithm is more effective than nonhybrid ones.
The success to design a hybrid optimization algorithm depends on how to make full use of the effect of exploration and exploitation carried by agents. To improve the exploration and exploitation property of the agents...
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The success to design a hybrid optimization algorithm depends on how to make full use of the effect of exploration and exploitation carried by agents. To improve the exploration and exploitation property of the agents, we present a hybrid optimization algorithm with both local and global search capabilities by combining the global search property of rain forest algorithm (RFA) and the rapid convergence of PSO. Originally two kinds of agents, RFAAs and PSOAs, are introduced to carry out exploration and exploitation, respectively. In order to improve population diversification, uniform distribution and adaptive range division are carried out by RFAAs in flexible scale during the iteration. A further improvement has been provided to enhance the convergence rate and processing speed by combining PSO algorithm with potential guides found by both RFAAs and PSOAs. Since several contingent local minima conditions may happen to PSO, special agent transformation is suggested to provide information exchanging and cooperative coevolution between RFAAs and PSOAs. Effectiveness and efficiency of the proposed algorithm are compared with several algorithms in the various benchmark function problems. Finally, engineering design optimization problems taken from the gait control of a snake-like robot are implemented successfully by the proposed RFA-PSO.
Mating of components is a major production step in automated assembly lines. Many aspects in mating two parts can be reduced to a peg-in-hole problem. When the parts are fitted to each other, jamming may occur, leadin...
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Mating of components is a major production step in automated assembly lines. Many aspects in mating two parts can be reduced to a peg-in-hole problem. When the parts are fitted to each other, jamming may occur, leading to assembly failure or damage to the parts, to the robot, or to its enviroment. Therefore, an intrinsically safe environment is required for the developement and testing of new algorithms. In this paper we present a workflow for developing control algorithms by means of a Virtual Testbed: First, we create a virtual setup to test and optimize the algorithm in simulation. This setup comprises a digital twin of the used physical manipulator and an application-oriented virtual environment for its operation. Then an algorithm for peg-in-hole insertion is developed that copes successfully with peg and hole fitted to each other with small clearances. This algorithm is tested and validated using the digital twin within the simulation. After its successfully validation in a virtual testbed, the algorithm is transfered to a physical setup containing the physical KUKA LWR4 manipulator and manufactured assembly parts.
Existing methods on structural controllability of networked systems are based on critical assumptions such as nodal dynamics with infinite time constants and availability of input signals to all nodes. In this paper, ...
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Existing methods on structural controllability of networked systems are based on critical assumptions such as nodal dynamics with infinite time constants and availability of input signals to all nodes. In this paper, we relax these assumptions and examine the structural controllability for practical model of networked systems. We explore the relationship between structural controllability and graph reachability. Consequently, a simple graph-based algorithm is presented to obtain the minimum driver nodes. Finally, simulation results are presented to illustrate the performance of the proposed algorithm in dealing with large-scale networked systems.
Fault prediction is an effective and important approach to improve the reliability and reduce the risk of accidents for complex electromechanical systems. In order to use the quantitative information and qualitative k...
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Fault prediction is an effective and important approach to improve the reliability and reduce the risk of accidents for complex electromechanical systems. In order to use the quantitative information and qualitative knowledge efficiently to predict the fault, a new model is proposed on the basis of belief rule base (BRB). Moreover, an evidential reasoning (ER) based optimal algorithm is developed to train the fault prediction model. The screw failure in computer numerical control (CNC) milling machine servo system is taken as an example and the fault prediction results show that the proposed method can predict the behavior of the system accurately with combining qualitative knowledge and some quantitative information.
It is difficult to well distinguish the dimensionless indexes between normal petrochemical rotating machinery equipment and those with complex faults. When the conflict of evidence is too big, it will result in uncert...
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It is difficult to well distinguish the dimensionless indexes between normal petrochemical rotating machinery equipment and those with complex faults. When the conflict of evidence is too big, it will result in uncertainty of diagnosis. This paper presents a diagnosis method for rotation machinery fault based on dimensionless indexes combined with K-nearest neighbor (KNN) algorithm. This method uses a KNN algorithm and an evidence fusion theoretical formula to process fuzzy data, incomplete data, and accurate data. This method can transfer the signals from the petrochemical rotating machinery sensors to the reliability manners using dimensionless indexes and KNN algorithm. The input information is further integrated by an evidence synthesis formula to get the final data. The type of fault will be decided based on these data. The experimental results show that the proposed method can integrate data to provide a more reliable and reasonable result, thereby reducing the decision risk.
Channel simulators are powerful tools that permit performance tests of the individual parts of a wireless communication system. This is relevant when new communication algorithms are tested, because it allows us to de...
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Channel simulators are powerful tools that permit performance tests of the individual parts of a wireless communication system. This is relevant when new communication algorithms are tested, because it allows us to determine if they fulfill the communications standard requirements. One of these tests consists of evaluating the system performance when a communication channel is considered. In this sense, it is possible to model the channel as an FIR filter with time-varying random coefficients. If the number of coefficients is increased, then a better approach to real scenarios can be achieved;however, in that case, the computational complexity is increased. In order to address this issue, a design methodology for computing the time-varying coefficients of the fading channel simulators using consumer-designed graphic processing units (GPUs) is proposed. With the use of GPUs and the proposed methodology, it is possible for nonspecialized users in parallel computing to accelerate their simulation developments when compared to conventional software. Implementation results show that the proposed approach allows the easy generation of communication channels while reducing the processing time. Finally, GPU-based implementation takes precedence when compared with the CPU-based implementation, due to the scattered nature of the channel.
The main objective of this article is to solve inverse heat conduction problems with the particle swarm optimization method. An enhanced particle swarm optimization (EPSO) algorithm is proposed to overcome the shortco...
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The main objective of this article is to solve inverse heat conduction problems with the particle swarm optimization method. An enhanced particle swarm optimization (EPSO) algorithm is proposed to overcome the shortcoming of earlier convergence of standard PSO algorithms. The EPSO is used to estimate the unknown time-dependent heat source in complex regions. Numerical experiments indicate the validity and stability of the EPSO method.
An economic user-centric WiFi offloading algorithm is proposed to satisfy the major concerns of wireless users, who wish to have better network performance with even less network expense. Thus in this paper both syste...
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An economic user-centric WiFi offloading algorithm is proposed to satisfy the major concerns of wireless users, who wish to have better network performance with even less network expense. Thus in this paper both system throughput and network expense are considered, and the goal of the proposed offloading algorithm is to obtain an optimal offloading ratio, which can both maximize the system throughput and minimize the network expense. Firstly, a practical system model is set up on the basis of a typical scenario of heterogeneous network. In this model, the average throughput of both cellular network and WiFi network is analyzed carefully. Then an economic user-centric WiFi offloading algorithm is proposed with an evaluation function to evaluate the system, and the optimal offloading ratio can be obtained by minimizing the evaluation function. At last, numerical results represent a direct calculating process of the optimal offloading ratio. These results in return validate the efficiency of the proposed offloading algorithm as well.
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