Feature selection based on particle swarm optimization is often employed for promoting the performance of artificial intelligence algorithms. However, its interpretability has been lacking of concrete research. Improving the stability of the feature selection method is a way to effectively improve its interpretability. A novel feature selection approach named Interpretable Particle Swarm Optimization is developed in this paper. It uses four data perturbation ways and three filter feature selection methods to obtain stable feature subsets, and adopts Fuch map to convert them to initial particles. Besides, it employs similarity mutation strategy, which applies Tanimoto distance to choose the nearest 1/3 individuals to the previous particles to implement mutation. Eleven representative algorithms and four typical datasets are taken to make a comprehensive comparison with our proposed approach. Accuracy, F1, precision and recall rate indicators are used as classification measures, and extension of Kuncheva indicator is employed as the stability measure. Experiments show that our method has a better interpretability than the compared evolutionary algorithms. Furthermore, the results of classification measures demonstrate that the proposed approach has an excellent comprehensive classification performance.

In view of the frequent and complex changes of GNSS visible satellite constellation in attitude determination system, an improved attitude signal simulation algorithm for high dynamic satellite signal simulator is proposed. Based on Software Radio architecture, elevation calculation in the antenna coordinate system and channel state control logic under the condition of carrier attitude changes are introduced into the algorithm to implement synchronous scheduling of visible satellite constellation and attitude signal simulation. This work guarantees the simulator to run constantly and stably for a long time with the advantages of high precision and low complexity. Compared with synchronous positioning results from the receiver, the simulation results show that not only can the output signals of the simulator accurately reflect the carrier's attitude characteristics, but also no step error is generated and the positioning precision is not influenced when visible satellite constellation changes.

Electromagnetic scattering properties of metamaterial cylinders due to a line source are studied by a multilayer algorithm based on eigenfunctional expansion. Closed forms of electric and magnetic fields are formulated. Both the fields inside the cylinder and field in outer space are plotted for different sizes of the cylinder. The focusing phenomena and the wave propagation in the presence of metamaterial cylinders are investigated and shown. Electromagnetic field distributions are presented for subwavelength metamaterial cylinders and cylinders fabricated by magnetoelectric materials, and resonant scattering and focusing properties are reported. Special designs of scatterer cloaking are proposed and calculated by multilayer algorithm which can reduce scattering cross sections.

Feature selection is important for learning algorithms, and it is still an open problem. Antlion optimizer is an excellent nature inspired method, but it doesn't work well for feature selection. This paper proposes a hybrid approach called Ant-Antlion Optimizer which combines advantages of antlion's smart behavior of antlion optimizer and ant's powerful searching movement of ant colony optimization. A mutation operator is also adopted to strengthen exploration ability. Comprehensive experiments by binary classification problems show that the proposed algorithm is superiority to other state-of-art methods on four performance indicators.

Background: The applying of third-party libraries is an integral part of many applications. But the libraries choosing is time-consuming even for experienced developers. The automated recommendation system for libraries recommendation is widely researched to help developers to choose libraries. Aim: from software engineering aspect, our research aims to give developers a reliable recommended list of third-party libraries at the early phase of software development lifecycle to help them build their development environment faster; and from technical aspect, our research aims to build a generalizable recommendation system framework which combines collaborative filtering and topic modeling techniques, in order to improve the performance of libraries recommendation significantly. Our works on this research: 1) we design a hybrid methodology to combine collaborative filtering and LDA text mining technology; 2) we build a recommendation system framework successfully based on the above hybrid methodology; 3) we make a well-designed experiment to validate the methodology and framework which use the data of 1,013 mobile application projects; 4) we do the evaluation for the result of the experiment. Conclusions: 1) hybrid methodology with collaborative filtering and LDA can improve the performance of libraries recommendation significantly; 2) based on the hybrid methodology, the framework works very well on the libraries recommendation for helping developers' libraries choosing. Further research is necessary to improve the performance of the libraries recommendation including: 1) use more accurate NLP technologies improve the correlation analysis; 2) try other similarity calculation methodology for collaborative filtering to rise the accuracy; 3) on this research, we just bring the time-series approach to the framework and make an experiment as comparative trial, the result shows that the performance improves continuously, so in further research we plan to use time-series data-mining as the basic methodology to update the framework.

Multi-objective evolutionary algorithms are widely used in many engineering optimization problems and artificial intelligence applications. Ant lion optimizer is an outstanding evolutionary method, but two issues need to be solved to extend it to the multi-objective optimization field, one is how to update the Pareto archive, and the other is how to choose elite and ant lions from archive. We develop a novel multi-objective variant of ant lion optimizer in this paper. A new measure combining Pareto dominance relation and distance information of individuals is put forward and used to tackle the first issue. The concept of time weight is developed to handle the second problem. Besides, mutation operation is adopted on solutions in middle part of archive to further improve its performance. Eleven functions, other four algorithms and four indicators are taken to evaluate the new method. The results show that proposed algorithm has better performance and lower time complexity.

This letter proposes a new transceiver for OFDM systems based on Smooth Local Trigonometric Transform (LTT). In our transceiver, the transmitter is realized by first modulating the original serial data using a constellation mapper, then feeding the results into the inverse LTT modulator. Unlike the conventional DFT-OFDM system, which always uses the roll cosine function as its window function, the proposed system needs no additional window function for the reason that LTT transform includes a bell-shaped window function by itself. Moreover, each LTT-OFDM symbol has a much more rapid attenuation rate outside of the spectral bandwidth and better spectrum convergence. In the receiver, the original data is recovered by demodulating the received data using forward LTT. Comparative simulation results from the conventional DFT-OFDM system, the system we proposed, and the recently proposed DCT based OFDM system are discussed in terms of bit error rate (BER).

Bolts in the bogie box of metro vehicles are fasteners which are significant for bogie box structure. Effective loosening bolts detection in early stage can avoid the bolt loss and accident occurrence. Recently, detection methods based on machine vision are developed for bolt loosening. But traditional image processing and machine learning methods have high missed rate and false rate for bolts detection due to the small size and complex background. To address this problem, a loosening bolts defection method based on deep learning is proposed. The proposed method cascades two stages in a coarse-to-fine manner, including location stage based on the Single Shot Multibox Detector (SSD) and the improved SSD sequentially localizing the bogie box and bolts and a semantic segmentation stage with the U-shaped Network (U-Net) to detect the looseness of the bolts. The accuracy and effectiveness of the proposed method are verified with images captured from the Shanghai Metro Line 9. The results show that the proposed method has a higher accuracy in detecting the bolts loosening, which can guarantee the stable operation of the metro vehicles.

In this paper, a joint blind synchronization and demodulation scheme is developed for ultra-wideband (UWB) impulse radio systems. Based on the prior knowledge of the direct-sequence (DS) spread codes, the proposed approach can achieve frame-level synchronization with the help of frame-rate samples. Taking advantage of the periodicity of the DS spread codes, the frame-level synchronization can be carried out even in one symbol interval. On the other hand, after timing acquisition, these frame-rate samples can be re-utilized also for demodulation. Thus the acquisition time and the implementation complexity are reduced considerably. The performance improvement can be justified by both theoretical analysis and simulation results, in terms of acquisition probability and bit error rate (BER).

To improve simulation precision, the signal model of navigation satellite signal simulators is illustrated, and the generation mechanism and evaluation criteria of an important error source-phase jitter in baseband signal generation, are studied subsequently. An improved baseband signal generator based on dual-ROM look-up table structure is designed with the application of a newly-established concept-virtual sampling rate. Pre-storage of typical baseband signal data and sampling rate conversion adaptive to Doppler frequency shifts are adopted to achieve the high-precision simulation of baseband signals. Performance analysis of the proposed baseband signal generator demonstrates that it can successfully suppress phase jitter and has better spectral performance, generating high-precision baseband signals, which paves the way to improving the overall precision of navigation satellite signal simulators.