Individual-based multi-objective optimal structured treatment interruption for HIV infection

In this paper, based on the measurable quantities from an individual patient that has infection to human immunodeficiency virus (HIV) and his/her condition is near to acquired immune deficiency syndrome (AIDS), individual-based multi-objective optimal treatments have been proposed. Firstly, the most effective parameters of the patient in computing Long-term non-progressor (LTNP) equilibrium are derived using global sensitivity analysis (GSA). To accomplish GSA effectively, Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC) are utilized to rank each of the parameters based on each state of the 5-dimensional model. Then, these results are used by Dempster–Shafer (D–S) evidence theory (DSET) to rank the most effective parameters comprehensively. Now, these effective identified parameters are estimated using extended Kalman filter (EKF), which its covariance matrices are optimized based on particle swarm optimization (PSO) algorithm. Thus, the proposed methodology gives a calibrated model corresponding to the individual patient. Based on this calibrated model, the LTNP equilibrium related to the individual patient is derived. Using the derived individual-based LTNP equilibrium optimal structured treatment interruption (STI) strategies are extracted by defining suitable multi-objective optimization problem and solving it through using non-dominated sorting genetic algorithm-II (NSGA-II). The results demonstrate that the proposed optimal treatments are able to effectively reach LTNP equilibrium with using the minimum and maximum drug usage of 3.6% and 35.1% of full drug usage treatment. Meanwhile, the different optimal treatments give the decision-makers enough flexibility to choose the suitable treatment based on existing facilities and necessities.     

Hierarchical sampling optimization of particle filter for global robot localization in pervasive network environment

This paper presents a hierarchical framework for managing the sampling distribution of a particle filter (PF) that estimates the global positions of mobile robots in a large‐scale area. The key concept is to gradually improve the accuracy of the global localization by fusing sensor information with different characteristics. The sensor observations are the received signal strength indications (RSSIs) of Wi‐Fi devices as network facilities and the range of a laser scanner. First, the RSSI data used for determining certain global areas within which the robot is located are represented as RSSI bins. In addition, the results of the RSSI bins contain the uncertainty of localization, which is utilized for calculating the optimal sampling size of the PF to cover the regions of the RSSI bins. The range data are then used to estimate the precise position of the robot in the regions of the RSSI bins using the core process of the PF. The experimental results demonstrate superior performance compared with other approaches in terms of the success rate of the global localization and the amount of computation for managing the optimal sampling size.     

Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings

Rolling element bearings (REBs) play an essential role in modern machinery and their condition monitoring is significant in predictive maintenance. Due to the harsh operating conditions, multi-fault may co-exist in one bearing and vibration signal always exhibits low signal-to-noise ratio (SNR), which causes difficulties in detecting fault. In the previous studies, maximum correlated kurtosis deconvolution (MCKD) has been validated as an efficient method to extract fault feature in the fault signals. Nonetheless, there are still some challenges when MCKD is applied to fault detection owing to the rigorous requirements of multiple input parameters. To overcome limitation, a multi-objective iterative optimization algorithm (MOIOA) for multi-fault diagnosis is proposed. In this method, correlated kurtosis (CK) is taken as a criterion to select optimal Morlet wavelet filter using the whale optimization algorithm (WOA). Meanwhile, to further eliminate the effect of the inaccurate period on CK, the update process of period is incorporated. After that, the simulated and experimental signals are utilized to testify the validity and superiority of the MOIOA for multiple faults detection by the comparison with MCKD. The results indicate that MOIOA is efficient to extract weak fault features even with heavy noise and harmonic interferences.     

Design of switchable band‐notched frequency selective absorber

An active band‐notched frequency selective absorber (BNFSA) with switchable notch band is proposed in this article. The BNFSA is a two‐layer structure composed of a lossy layer at the top and a ground plane at the bottom, separated by an air spacer. The element of the lossy layer is a lumped‐resistor‐loaded metallic dipole with a parallel LC resonance structure, which is realized by complementary n‐shaped resonator (CnR) inserted in the center, and PIN diode is welded at two arms of CnR. The bias circuit printed on the back of the substrate of the lossy layer connects to anode and cathode of the diode by via hole and isolates by the inductor. Simulation results show that the notch bands are located at 4.50 and 6.81 GHz when the diode sets to ON and OFF, respectively. To validate the performance of switchable BNFSA, the prototypes are fabricated and measured, reasonable agreement between simulated and measured results is obtained.     

On‐site wastewater treatment using reactive filter media

The aim of this study is to reduce the phosphate concentration in treated wastewater using a small amount of the reactive filter media, Filtralite P. Biologically treated wastewater was passed through a filter with 215 g of Filtralite P. In the laboratory, the phosphate removal efficiency was 51% at a flow speed of 0.87 m/h. Under real conditions, in an experimental stand filled with 0.5 m³ of Filtralite P, the phosphate removal efficiency was 85% and the removal efficiency of total suspended solids was 57% after a 5‐month period. The residual phosphate concentration in the filtrate from the experimental stand was 1 mg/L of PO₄‐P after the 5‐month period. The experimental filtration plant was buried in the ground, and it did not freeze and worked well under winter conditions. The results of this study can be useful in the design and development of tertiary wastewater treatment plants in view of their sustainability potential.     

DCNN深度特征与交替方向乘子的相关滤波跟踪

针对采用相关滤波的判别式目标跟踪遇到的瓶颈问题：由于目标快速移动引起边界效应，使得相关滤波器在学习与更新过程中可能会引入错误，最终错误的累积将导致跟踪失败。在采集深度学习特征与样本相似性度量的基础上，提出一种引入交替方向乘子方法的改进相关滤波目标跟踪算法，选择DCNN深度特征有效地表征待跟踪目标的初始状态，通过在线分类过程中样本相似性比对与半监督学习，辅助解决相关滤波器在学习过程中存在的自学习问题。所提目标跟踪算法特别适合训练样本为持续获得的、同时存储空间较小的机器学习过程，提高目标在快速运动与部分遮挡等复杂情况下的跟踪成功率，针对VOT2016标准测试视频的实验表明：当目标面临快速运动时，对比CN、SAMF、STC算法，所提DA-CFT跟踪算法将跟踪成功率分别由60.4%～73.4%、67.2%～82.9%、80.9%～88.1%提升至85.6%～91.0%。     

基于并行跟踪检测框架与深度学习的目标跟踪算法

在空地协同背景下，地面目标的移动导致其在无人机视角下外观会发生较大变化，传统算法很难满足此类场景的应用要求。针对这一问题，提出基于并行跟踪和检测（PTAD）框架与深度学习的目标检测与跟踪算法。首先，将基于卷积神经网络（CNN）的目标检测算法SSD作为PTAD的检测子处理关键帧获取目标信息并提供给跟踪子；其次，检测子与跟踪子并行处理图像帧并计算检测与跟踪结果框的重叠度及跟踪结果的置信度；最后，根据跟踪子与检测子的跟踪或检测状态来判断是否对跟踪子或检测子进行更新，并对图像帧中的目标进行实时跟踪。在无人机视角下的视频序列上开展实验研究和对比分析，结果表明所提算法的性能高于PTAD框架下最优算法，而且实时性提高了13%，验证了此算法的有效性。     

Measurement of vertical and horizontal vibrations of a probe for acoustic evaluation of food texture

Two‐dimensional (vertical and horizontal) vibrations of a wedge‐type probe upon food rupture were evaluated separately using two accelerometers placed perpendicular to a guide rod of a swing‐arm device for texture evaluation of the flesh of three varieties of apples and three types of potato chips. Voltage signals from the accelerometers were filtered using a half‐octave band‐pass filter. The energy texture index (ETI), based on kinetic energy of the vertical or horizontal probe vibrations, was calculated over low to high frequency bands (no. 1–21). The spectra for the flesh of the three varieties of apples included two common peaks for vertical ETI at band no. 11 (1,120–1,600 Hz) and 19 (17,920–25,600 Hz) and horizontal ETI at band no. 1 (0–10 Hz) and 15 (4,480–6,400 Hz). The spectra for the three types of potato chips had a common ETI peak at band no. 11 (1,120–1,600 Hz) for horizontal ETI and at no. 15 (4,480–6,400 Hz) for vertical ETI. The three apple varieties gave rise to different intensities of vertical and horizontal ETIs while the two peaks were maintained. The thick potato chip type had higher vertical and horizontal ETIs than the thin and soft varieties in most bands; however, the thin type had the highest vertical ETIs only in lower bands (0–200 Hz). The soft type had the lowest vertical and horizontal ETI. The above results suggest that different food textures can be distinguished by two‐dimensional vibration analyses of probe insertion into a food sample based on frequency bands.     

Full and reduced-order H∞ filtering of Takagi–Sugeno fuzzy time-varying delay systems: Input–output approach

This article addresses the issue of delay-dependent H_∞ filtering design for TakagiŮSugeno fuzzy time-varying delay systems using the input–output approach. A three-term approximation model has been used to transform the original system into two interconnected subsystems. Since the nonquadratic Lyapunov–Krasovskii functional requires to deal with the membership function's (MF) time derivative, upper-bound inequalities have been added to the obtained conditions. Based on the scaled small gain theorem, nonquadratic Lyapunov–Krasovskii functional approach and considering the bounds of the MF time-derivative, the H_∞ full- and reduced-order filters are designed and then formulated in terms of linear matrix inequalities. Finally, illustrative examples are presented to demonstrate the validity of the proposed methods.