In sequential recommender systems, the main problems are the long-tailed distribution of data and noise interference. A Contrastive Framework for Sequential Recommendation (CFSeRec) is proposed to solve these two prob...
In sequential recommender systems, the main problems are the long-tailed distribution of data and noise interference. A Contrastive Framework for Sequential Recommendation (CFSeRec) is proposed to solve these two problems respectively. Token shuffling and adversarial attack data augmentation methods are used in the framework to improve the quality and quantity of training data, so that the long-tailed problem is mitigated. Through the application of projection head method, the sequence representation becomes more general and robust, rather than just adapted to the task of contrastive learning. Therefore, the impact of noise on sequence recommender systems is effectively alleviated. Experiments on four public datasets show that CFSeRec achieves state-of-the-art performance in the metrics of hit ratio and normalized discounted cumulative gain, when comparing to the seven previous frameworks.
With the aim of 2-AMT electric vehicles, a comprehensive shift schedule that considers both power and economy is proposed. First, the objective function of the comprehensive shift schedule is constructed, which is the...
With the aim of 2-AMT electric vehicles, a comprehensive shift schedule that considers both power and economy is proposed. First, the objective function of the comprehensive shift schedule is constructed, which is the weighted sum of vehicle acceleration time and vehicle power consumption per unit distance. Second, Sparrow Search Algorithm is used to solve the objective function and obtain the comprehensive shift schedule. Finally, AVL Cruise simulation software is used to simulate vehicle dynamics and economy under an NEDC. The results show that the comprehensive shift schedule is similar to the optimal power shift schedule in terms of dynamic performance, and the economy is optimized by 3%. The feasibility of the comprehensive shift schedule is verified.
Many real-world optimization problems involve multiple conflicting objectives. Such problems are called multiobjective optimization problems(MOPs). Typically, MOPs have a set of so-called Pareto optimal solutions rath...
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Many real-world optimization problems involve multiple conflicting objectives. Such problems are called multiobjective optimization problems(MOPs). Typically, MOPs have a set of so-called Pareto optimal solutions rather than one unique optimal solution. To assist the decision maker(DM) in finding his/her most preferred solution, we propose an interactive multiobjective evolutionary algorithm(MOEA)called iDMOEA-εC, which utilizes the DM's preferences to compress the objective space directly and progressively for identifying the DM's preferred region. The proposed algorithm employs a state-of-the-art decomposition-based MOEA called DMOEA-εC as the search engine to search for solutions. DMOEA-εC decomposes an MOP into a series of scalar constrained subproblems using a set of evenly distributed upper bound vectors to approximate the entire Pareto front. To guide the population toward only the DM's preferred part on the Pareto front, an adaptive adjustment mechanism of the upper bound vectors and two-level feasibility rules are proposed and integrated into DMOEA-εC to control the spread of the population. To ease the DM's burden, only a small set of representative solutions is presented in each interaction to the DM,who is expected to specify a preferred one from the set. Furthermore, the proposed algorithm includes a two-stage selection procedure, allowing to elicit the DM's preferences as accurately as possible. To evaluate the performance of the proposed algorithm, it was compared with other interactive MOEAs in a series of experiments. The experimental results demonstrated the superiority of iDMOEA-εC over its competitors.
The combined control of variable speed and variable displacement is a new type of volume control with high efficiency and fast response. However, due to the inherent nonlinearity of multiplication, it brings certain d...
The combined control of variable speed and variable displacement is a new type of volume control with high efficiency and fast response. However, due to the inherent nonlinearity of multiplication, it brings certain difficulties to the control. The electric double-variable pump[1] is a dual-input single-output system, and it is a nonlinear system[2]. It is necessary to linearize the system or use a nonlinear control method to control and solve the control problem of the system. In this paper, an intelligentcontrol rule is proposed for the nonlinear problem of double input and single output. Through backstepping design[3], the nonlinear system is transformed into multiple linear subsystems. Then the original system is turned into two independent subsystems with single input and single output, which are controlled separately. The co-simulation platform based on AMESIM and Simulink[4] has been verified and compared with a single PID control algorithm to simulate the step response and sinusoidal tracking performance of the system. The results show that the response speed of the system has been greatly improved.
Safe and efficient decision-making and path planning is a challenging problem for autonomous driving in highway because of numerous dynamic vehicles around the ego-vehicle. For ego-vehicle driving on structured roads,...
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Uncontrolled intersections are important and challenging traffic scenarios for autonomous vehicles. Vehicles not only need to avoid collisions with dynamic vehicles instantaneously but also predict their behavior then...
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In this paper,we develop a distributed solver for a group of strict(non-strict)linear matrix inequalities over a multi-agent network,where each agent only knows one inequality,and all agents co-operate to reach a cons...
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In this paper,we develop a distributed solver for a group of strict(non-strict)linear matrix inequalities over a multi-agent network,where each agent only knows one inequality,and all agents co-operate to reach a consensus solution in the intersection of all the feasible *** formulation is transformed into a distributed optimization problem by introducing slack variables and consensus ***,by the primal–dual methods,a distributed algorithm is proposed with the help of projection operators and derivative ***,the convergence of the algorithm is analyzed,followed by illustrative simulations.
In order to solve the problem that the clustering number in Fuzzy C-Means(FCM) needs to be set manually in advance,a two-phase hybrid fuzzy clustering approach using membership fusion(TPHFC) is *** the first phase,con...
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In order to solve the problem that the clustering number in Fuzzy C-Means(FCM) needs to be set manually in advance,a two-phase hybrid fuzzy clustering approach using membership fusion(TPHFC) is *** the first phase,conventional FCM is used for *** the second phase,the results obtained by pre-clustering are fused according to the relationship between the membership of samples to different clusters and the membership threshold.A density-based clustering validity measurement is established for this *** proposed method obtains better clustering effect with setting fewer *** on synthetic datasets conforming to Gaussian distribution and UCI datasets demonstrate the effectiveness of the proposed clustering *** clustering number and clustering centers can be obtained adaptively.
This paper deals with the problem of 4D pose estimation for large unmanned aerial vehicles (UAVs) in close range. A sensor system consisting of one single point laser range-finder and two cameras is designed and a nov...
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ISBN:
(纸本)9781665465373
This paper deals with the problem of 4D pose estimation for large unmanned aerial vehicles (UAVs) in close range. A sensor system consisting of one single point laser range-finder and two cameras is designed and a novel pose estimation method based on vision fusion and point cloud registration is proposed. Our approach works on one-shot mode and only requires 10 samples with real poses for template construction. Through V-rep simulation environment, we generate two 200-sample datasets of different difficulty for evaluation. Error quantiles, 5cm5deg and 10cml0deg are three evaluation metrics used in our ablation experiments. It is illustrated that our method outperforms in robustness and precision due to proposed dimension extension modification and fusion of vision sensors.
Aiming at the problems containing complex working conditions,modeling difficulties and long-time delay of thermoelectric cooler temperature control system,an improved ADRC control method combining ADRC and Smith predi...
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Aiming at the problems containing complex working conditions,modeling difficulties and long-time delay of thermoelectric cooler temperature control system,an improved ADRC control method combining ADRC and Smith predictor is proposed in this *** deals with the disturbances and uncertain dynamics in the system,SP compensates the time delay to improve the control *** with the traditional PID controller,the proposed control method has faster response speed and stronger anti-disturbance ability,meanwhile,overcomes the dependence of Smith predictor on object parameters to a certain *** simulation and experimental verification,a good control effect is obtained,which provides a positive guidance of the related application of thermoelectric cooler.
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