Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Synthetic aperture radar. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

A hybrid discrete artificial bee colony algorithm for permutation flowshop scheduling problem

Abstract: To minimize the makespan in permutation flowshop scheduling problems, a hybrid discrete artificial bee colony (HDABC) algorithm is presented. In the HDABC, each solution to the problem is called a food source and represented by a discrete job permutation. First, the initial population with certain quality and diversity is generated from Greedy Randomized Adaptive Search Procedure (GRASP) based on Nawaz-Enscore-Ham (NEH) heuristics. Second, the discrete operators and algorithm, such as insert, swap, path relinking and GRASP are applied to generate new solution for the employed bees, onlookers and scouts. Moreover, local search is applied to the best one. The presented algorithm is tested on scheduling problem benchmarks. Experimental results show its efficiency.

Location-Aware Service Recommendation With Enhanced Probabilistic Matrix Factorization

Abstract: Owing to the ever-growing popularity of mobile computing, a large number of services have been developed for a variety of users. Considering this, recommending useful services to users is an urgent problem that needs to be addressed. Collaborative filtering (CF) approaches have been successfully adopted for services recommendation. Nevertheless, the prediction accuracy of the existing CF approaches is likely to reduce due to many reasons, such as inability to use side information and high data sparsity, which further lead to low quality of services recommendation. In order to solve these problems, some model-based CF approaches have been proposed. In this paper, we propose a novel quality of service prediction approach based on probabilistic matrix factorization (PMF), which has the capability of incorporating network location (an important factor in mobile computing) and implicit associations among users and services. First, we propose a novel clustering method that is capable of utilizing network location to cluster users. Based on the clustering results, we further propose an enhanced PMF model. The proposed model also incorporates the implicit associations among users and services. In addition, our model incorporates the implicit relationships between the users and the services. We conducted experiments on one real-world data set, and the experimental results show that our model outperforms the compared methods.

New Petri Net Structure and Its Application to Optimal Supervisory Control: Interval Inhibitor Arcs

Abstract: This paper presents a new Petri net structure, namely, an interval inhibitor arc, and its application to the optimal supervisory control of Petri nets. An interval inhibitor arc is an arc from a place to a transition labeled with an integer interval. The transition is disabled by the place if the number of tokens in the place is between the labeled interval. The formal definition and the firing rules of Petri nets with interval inhibitor arcs are developed. Then, an optimal Petri net supervisor based on the interval inhibitor arcs is designed to prevent a system from reaching illegal markings. Two techniques are developed to simplify the supervisory structure by compressing the number of control places. The proposed approaches are general since they can be applied to any bounded Petri net models. A marking reduction approach is also introduced if they are applied to Petri net models of flexible manufacturing systems. Finally, a number of examples are provided to demonstrate the proposed approaches and the experimental results show that they can obtain optimal Petri net supervisors for some net models that cannot be optimally controlled by pure net supervisors. Furthermore, the obtained supervisor is structurally simple.

Differentially Fed, Dual-Band Dual-Polarized Filtering Antenna With High Selectivity for 5G Sub-6 GHz Base Station Applications

Abstract: In this communication, a novel dual-band dual-polarized filtering antenna with high selectivity is proposed. By etching four slots at each side of a square radiating patch, dual operation bands as well as a natural radiation null between the two bands are observed. Another two radiation nulls are generated by introducing a novel feeding structure, which is composed of a microstrip line with open-circuit stepped-impedance resonators (OCSIRs). Thus, a dual-band bandpass filtering response of the realized gain is obtained. Each polarization of the antenna is differentially fed, and the differential port isolation is better than 37 dB. A prototype of the antenna element and a $1\times 4$ array is fabricated and tested to validate our design. Measured results show that the antenna element has dual operation bands of 3.28–3.71 and 4.8–5.18 GHz for VSWR < 1.5. Besides, a high out-of-band gain-suppression level which reaches 17.7 dB is also obtained. Good performance of the proposed antenna makes it a promising candidate for 5G sub-6 GHz base station systems.