Medium-term forecast of daily passenger volume of high speed railway based on DLP-WNNMedium-term forecast of dailypassenger volume of high speedrailway based on DLP-WNN

Purpose – This paper aims to propose a medium-term forecast model for the daily passenger volume of HighSpeed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume formultiple consecutivedays (e.g. 120 days).Design/methodology/approach – By analyzing the characteristics of the historical data on daily passengervolume of HSR systems, the date and holiday labels were designed with determined value ranges.In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double LayerParallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of thedaily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result byweighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of dailypassenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume toensure the accuracy of medium-term forecast.Findings – According to the example application, in which the DLP-WNN modelwas used for the medium-termforecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the averageabsolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP)neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalizedregression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for themedium-term forecast of the daily passenger volume of HSR.Originality/value – This study proposed a Double Layer Parallel structure forecast model for medium-termdaily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and WaveletNeural Network. The predict results are important input data for supporting the line planning, scheduling andother decisions in operation and management in HSR systems.

50 m/187.5 Mbit/s real-time underwater wireless optical communication based on optical superimposition

In this paper,an optical pulse amplitude modulation with 4 levels(PAM-4)using a fiber combiner is proposed to enhance the data rate of a field-programmable gate-array-based long-distance real-time underwater wireless optical communication system.Two on-off keying signals with different amplitudes are used to modulate two pigtailed laser diodes,respectively,and the generated optical signals are superimposed into optical PAM-4 signals by a fiber combiner.The optical PAM-4scheme can effectively alleviate the nonlinearity,although it reduces the peak-to-peak value of the emitting optical power by 25%.A real-time data rate of 187.5 Mbit/s is achieved by using the optical PAM-4 with a transmission distance of 50 m.The data rate is increased by about 25%compared with the conventional electrical PAM-4 in the same condition.

Security enhancement for OFDM-UWOC system using three-layer chaotic encryption and chaotic DFT precoding

Security is one of the key issues in communications,but it has not attracted much attention in the field of underwater wireless optical communication(UWOC).This Letter proposes a UWOC encryption scheme with orthogonal frequency division multiplexing(OFDM)modulation,based on the three-layer chaotic encryption and chaotic discrete Fourier transform(DFT)precoding.The three-layer chaotic encryption processes are bit stream diffusion,in-phase/quadrature encryption,and time-frequency scrambling.With multi-fold data encryption,the scheme can create a keyspace of 9.7×10^(179),effectively resisting brute force attacks and chosen-plaintext attacks.A 3 Gbit/s encrypted OFDM signal is successfully transmitted over a 7 m water channel.