COMPRESSIBLE VIRTUAL WINDOW ALGORITHM IN PICKING PROCESS CONTROL OF AUTOMATED SORTING SYSTEM

Compared to fixed virtual window algorithm （FVWA）, the dynamic virtual window algorithm （DVWA） determines the length of each virtual container according to the sizes of goods of each order, which saves space of virtual containers and improves the picking efficiency. However, the interval of consecutive goods caused by dispensers on conveyor can not be eliminated by DVWA, which limits a further improvement of picking efficiency. In order to solve this problem, a compressible virtual window algorithm （CVWA） is presented. It not only inherits the merit of DVWA but also compresses the length of virtual containers without congestion of order accumulation by advancing the beginning time of order picking and reasonably coordinating the pace of order accumulation. The simulation result proves that the picking efficiency of automated sorting system is greatly improved by CVWA.

Knowledge Discovery from Communication Network Alarm Databases

The technique of Knowlege Discovery in Databases (KDD) to learn valuable knowledge hidden in network alarm databases is introduced. To get such knowledge, we propose an efficient method based on sliding windows (named as Slidwin) to discover different episode rules from time squential alarm data. The experimental results show that given different thresholds parameters, large amount of different rules could be discovered quickly.

A recursive calculating algorithm for higher-order cumulants over sliding window and its application in speech endpoint detection

Regarding the performance of traditional endpoint detection algorithms degrades as the environment noise level increases, a recursive calculating algorithm for higher-order cu- mulants over a sliding window is proposed. Then it is applied to the speech endpoint detection. Furthermore, endpoint detection is carried out with the feature of energy. Experimental results show that both the computational efficiency and the robustness against noise of the proposed algorithm are improved remarkably compared with traditional algorithm. The average prob- ability of correct point detection （Pc-point） of the proposed voice activity detection （VAD） is 6.07% higher than that of G.729b VAD in different noisy at different signal-noise ratios （SNRs） environments.

High-accuracy and Low-complexity Phasor Estimation Method for PMU Calibration

Due to the increasing development of renewables in power systems,the requirements for phasor measurement units(PMUs)becomes higher.A PMU calibrator is an important tool to test and calibrate PMUs to ensure their measurement performance.This device can provide accurate reference values for error analysis of PMUs.In this paper,a phasor algorithm with low computational complexity and high accuracy is proposed for the PMU calibrator.This method reduces the processor requirements and development costs of the calibrator,thereby facilitating its popularization.At first,an enhanced discrete Fourier transform(DFT)method is put forward:1)the frequency response of the windowed DFT method is analyzed to reveal its large measurement errors under dynamic conditions;2)the parameter requirements of the DFT window that is regarded as a lowpass filter are analyzed,and thus a lowpass filter with better filtering performance is designed as the window coefficients to improve the estimation accuracy.Then,based on the enhanced DFT algorithm,a calibrator algorithm framework consisting of two-stage filters and a signal recognition module is established.This algorithm can consider the anti-interference ability and dynamic measurement accuracy at a low reporting rate.Simulation and experimental test results show that the proposed calibrator algorithm provides high-accuracy measurements of the static and dynamic signals with low computational complexity.

Adaptive fractional window increment of TCP in multihop ad hoc networks

We propose an adaptive fractional window increasing algorithm (AFW) to improve the performance of the fractional window increment (FeW) in (Nahm et al., 2005). AFW fully utilizes the bandwidth when the network is idle, and limits the op-erating window when the network is congested. We evaluate AFW and compare the total throughput of AFW with that of FeW in different scenarios over chain, grid, random topologies and with hybrid traffics. Extensive simulation through ns2 shows that AFW obtains 5% higher throughput than FeW, whose throughput is significantly higher than that of TCP-Newreno, with limited modi-fications.

Human motion prediction using optimized sliding window polynomial fitting and recursive least squares

Human motion prediction is a critical issue in human-robot collaboration(HRC)tasks.In order to reduce the local error caused by the limitation of the capture range and sampling frequency of the depth sensor,a hybrid human motion prediction algorithm,optimized sliding window polynomial fitting and recursive least squares(OSWPF-RLS)was proposed.The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input,and uses recursive least squares(RLS)to predict the human movement trajectories within the time window.Then,the optimized sliding window polynomial fitting(OSWPF)is used to calculate the multi-step prediction value,and the increment of multi-step prediction value was appropriately constrained.Experimental results show that compared with the existing benchmark algorithms,the OSWPF-RLS algorithm improved the multi-step prediction accuracy of human motion and enhanced the ability to respond to different human movements.