Kinematic calibration under the expectation maximization framework for exoskeletal inertial motion capture system

This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method.

Expectation-maximization (EM) Algorithm Based on IMM Filtering with Adaptive Noise Covariance

A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise covariance Q online. For the existing IMM filtering theory, the matrix Q is determined by means of design experience, but Q is actually changed with the state of the maneuvering target. Meanwhile it is severely influenced by the environment around the target, i.e., it is a variable of time. Therefore, the experiential covariance Q can not represent the influence of state noise in the maneuvering process exactly. Firstly, it is assumed that the evolved state and the initial conditions of the system can be modeled by using Gaussian distribution, although the dynamic system is of a nonlinear measurement equation, and furthermore the EM algorithm based on IMM filtering with the Q identification online is proposed. Secondly, the truncated error analysis is performed. Finally, the Monte Carlo simulation results are given to show that the proposed algorithm outperforms the existing algorithms and the tracking precision for the maneuvering targets is improved efficiently.

A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.

Capacity Maximization Based Power Loading Analysis for Digital Channelized Satcom Systems

For digital channelized frequency division multiple access based satellite communication(SATCOM) systems,it is a challenging but critical issue to improve the transponder power and spectrum efficiency simultaneously under limited and non-linear high-power amplifier conditions.In this paper,different from the traditional link supportability designs aiming at minimizing the total transponder output power,a maximal sum Shannon capacity optimization objective is firstly raised subject to link supportability constraints.Furthermore,an efficient multilevel optimization(MO) algorithm is proposed to solve the considered optimization problem in the case of single link for each terminal.Moreover,in the case of multiple links for one terminal,an improved MO algorithm involving Golden section and discrete gradient searching procedures is proposed to optimize power allocation over all links.Finally,several numerical results are provided to demonstrate the effectiveness of our proposals.Comparison results show that,by the MO algorithm,not only all links' supportability can be guaranteed but also a larger sum capacity can be achieved with lower complexity.

CRB:A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various rumors.In order to block the outbreak of rumor,one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor.The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues.Firstly,in order to simulate the dissemination of multiple types of information,we propose a competitive linear threshold model with state transition(CLTST)to describe the spreading process of rumor and anti-rumor in the same network.Subsequently,we put forward a community-based rumor blocking(CRB)algorithm based on influence maximization theory in social networks.Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes,which includes community detection,selection of candidate anti-rumor seeds and generation of anti-rumor seed set.Under the CLTST model,the CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance.Experimental results show that the proposed model can better reflect the process of rumor propagation,and review the propagation mechanism of rumor and anti-rumor in online social networks.Moreover,the proposed CRB algorithm has better performance in weakening the rumor dissemination ability,which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread,sensitivity analysis,seeds distribution and running time.

Sum-Rate Maximization in Active RIS-Assisted Multi-Antenna WPCN

In this paper,we propose an active reconfigurable intelligent surface(RIS)enabled hybrid relaying scheme for a multi-antenna wireless powered communication network(WPCN),where the active RIS is employed to assist both wireless energy transfer(WET)from the power station(PS)to energyconstrained users and wireless information transmission(WIT)from users to the receiving station(RS).For further performance enhancement,we propose to employ both transmit beamforming at the PS and receive beamforming at the RS.We formulate a sumrate maximization problem by jointly optimizing the RIS phase shifts and amplitude reflection coefficients for both the WET and the WIT,transmit and receive beamforming vectors,and network resource allocation.To solve this non-convex problem,we propose an efficient alternating optimization algorithm with the linear minimum mean squared error criterion,semidefinite relaxation(SDR)and successive convex approximation techniques.Specifically,the tightness of applying the SDR is proved.Simulation results demonstrate that our proposed scheme with 10 reflecting elements(REs)and 4 antennas can achieve 17.78%and 415.48%performance gains compared to the single-antenna scheme with 10 REs and passive RIS scheme with 100 REs,respectively.

Hybrid Block Diagonalization Precoding for Multi-User Weighted Sum-Rate Maximization

This paper studies large-scale multi-input multi-output(MIMO)orthogonal frequency division multiplexing(OFDM)communications in a broadband frequency-selective channel,where a massive MIMO base station(BS)communicates with multiple users equipped with multi-antenna.We develop a hybrid precoding design to maximize the weighted sum-rate(WSR)of the users by optimizing the digital and the analog precoders alternately.For the digital part,we employ block-diagonalization to eliminate inter-user interference and apply water-filling power allocation to maximize the WSR.For the analog part,the optimization of the PSN is formulated as an unconstrained problem,which can be efficiently solved by a gradient descent method.Numerical results show that the proposed block-diagonal hybrid precoding algorithm can outperform the existing works.

PERIODIC SYSTEMS WITH TIME DEPENDENT MAXIMAL MONOTONE OPERATORS

We consider a first order periodic system in R^(N),involving a time dependent maximal monotone operator which need not have a full domain and a multivalued perturbation.We prove the existence theorems for both the convex and nonconvex problems.We also show the existence of extremal periodic solutions and provide a strong relaxation theorem.Finally,we provide an application to nonlinear periodic control systems.

An Influence Maximization Algorithm Based on Improved K-Shell in Temporal Social Networks

Influence maximization of temporal social networks(IMT)is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread.To solve the IMT problem,we propose an influence maximization algorithm based on an improved K-shell method,namely improved K-shell in temporal social networks(KT).The algorithm takes into account the global and local structures of temporal social networks.First,to obtain the kernel value Ks of each node,in the global scope,it layers the network according to the temporal characteristic of nodes by improving the K-shell method.Then,in the local scope,the calculation method of comprehensive degree is proposed to weigh the influence of nodes.Finally,the node with the highest comprehensive degree in each core layer is selected as the seed.However,the seed selection strategy of KT can easily lose some influential nodes.Thus,by optimizing the seed selection strategy,this paper proposes an efficient heuristic algorithm called improved K-shell in temporal social networks for influence maximization(KTIM).According to the hierarchical distribution of cores,the algorithm adds nodes near the central core to the candidate seed set.It then searches for seeds in the candidate seed set according to the comprehensive degree.Experiments showthatKTIMis close to the best performing improved method for influence maximization of temporal graph(IMIT)algorithm in terms of effectiveness,but runs at least an order of magnitude faster than it.Therefore,considering the effectiveness and efficiency simultaneously in temporal social networks,the KTIM algorithm works better than other baseline algorithms.

3D placement of unmanned aerial vehicles and partially overlapped channel assignment for throughput maximization

This paper investigates a wireless system with multi-Unmanned Aerial Vehicles(UAVs)for improving the overall throughput.In contrast to previous studies that optimize the locations of UAVs and channel assignment separately,this paper considers the two issues jointly by exploiting Partially Overlapped Channels(POCs).The optimization problem of maximizing network throughput is formulated as a non-convex and non-linear problem.In order to find a practical solution,the problem is decomposed into two subproblems,which are iteratively optimized.First,the optimal locations of UAVs are determined under a fixed channel assignment scheme by solving the mixed-integer second-order cone problem.Second,an efficient POC allocation scheme is determined via the proposed channel assignment algorithm.Simulation results show that the proposed approach not only significantly improves system throughput and service reliability compared with the cases in which only orthogonal channels and stationary UAVs are considered,but also achieves similar performance using the exhaustive search algorithm with lower time complexity.

SPEECH ENHANCEMENT BASED ON SECOND ORDER ARCHITECTURE AND INFORMATION MAXIMIZATION THEORY

Based on the idea of adaptive noise cancellation (ANC), a second order architecture is proposed for speech enhancement. According as the Information Maximization theory, the corresponding gradient descend algorithm is proposed. With real speech signals in the simulation, the new algorithm demonstrates its good performance in speech enhancement. The main advantage of the new architecture is that clean speech signals can be got with less distortion.

Maximization of Acoustic Levitating Force for a Single-Axis Acoustic Levitation System Using the Finite Element Method

We investigate single-axis acoustic levitation using standing waves to levitate particles freely in a medium bounded by a driver and a reflector. The acoustic pressure at the pressure antinode of the standing wave counteracts the downward gravitational force of the levitating object. The optimal relationship between the air gap and the driving frequency leads to resonance and hence maximization of the levitating force. Slight deviation from the exact resonance condition causes a reduction in acoustic pressure at the pressure antinodes. This results in a significant reduction of the levitating force. The driving frequency is kept constant while the air gap is varied for different conditions. The optimal air gap for maximizing the levitation force is studied for first three resonance modes. Furthermore, a levitating particle is introduced between the driver and the reflector. The dependence of the resonance condition on the size of the levitating particle as well as the position of the particle between the driver and the reflector has also been studied. As the size of the levitating particle increases, the resonance condition also gets modified. Finite element results show a good agreement with the validated results available in the literature. Furthermore, the finite element approach is also used to study the variation of acoustic pressure at the pressure antinode with respect to the size of the reflector. The optimum diameter of the reflector is calculated for maximizing the levitating force for three resonance modes.

Performance Enhancement and Utility Maximization for TCP in Wireless Mesh Networks

In Wireless Mesh Networks (WMNs),the performance of conventional TCP significantly deteriorates due to the unreliable wireless channel.To enhance TCP performance in WMNs,TCP/LT is proposed in this paper.It introduces fountain codes into packet reorganization in the protocol stack of mesh gateways and mesh clients.Furthermore,it is compatible with conventional TCP.Regarded as a Performance Enhancement Proxies (PEP),a mesh gateway buffers TCP packets into several blocks.It simultaneously processes them by using fountain encoders and then sends them to mesh clients.Apart from the improvement of the throughput of a unitary TCP flow,the entire network utility maximization can also be ensured by adjusting the scale of coding blocks for each TCP flow adaptively.Simulations show that TCP/LT presents high throughput gains over single TCP in lossy links of WMNs while preserving the fairness for multiple TCPs.As losses increase,the transmission delay of TCP/LT experiences a slow linear growth in contrast to the exponential growth of TCP.

Incremental Influence Maximization for Dynamic Social Networks

Influence maximization is one fundamental and important problem to identify a set of most influential individuals to develop effective viral marketing strategies in social network. Most existing studies mainly focus on designing efficient algorithms or heuristics to find Top-K influential individuals for static network. However, when the network is evolving over time, the static algorithms have to be re-executed which will incur tremendous execution time. In this paper, an incremental algorithm DIM is proposed which can efficiently identify the Top-K influential individuals in dynamic social network based on the previous information instead of calculating from scratch. DIM is designed for Linear Threshold Model and it consists of two phases: initial seeding and seeds updating. In order to further reduce the running time, two pruning strategies are designed for the seeds updating phase. We carried out extensive experiments on real dynamic social network and the experimental results demonstrate that our algorithms could achieve good performance in terms of influence spread and significantly outperform those traditional static algorithms with respect to running time.

The impact of accountability on local officials’behavior in the pandemic prevention and control in China based on utility maximization

Background:The COVID-19 pandemic is a public health crisis and an inspection of national governance systems and crisis response capabilities of countries globally.China has adopted a tough accountability system for officials and has succeeded in containing the spread of the pandemic.This study aimed to assess the impact of accountability on local officials’behavior in the pandemic prevention and control based on the official promotion tournament theory and utility maximization analysis framework.Methods:The panel data of 237 Chinese cities were extracted with local officials’characteristics,confirmed cases,Baidu migration index,Baidu search index according to city names,and data were excluded with local officials’relocation or sub-provincial cities between January 1,2020 and May 5,2020.Promotion gain and accountability cost were constructed by adopting promotion speed indicator,and the research hypotheses were assumed based on the utility maximization.It was the first time to apply the interaction model to empirically investigate the relationship between the promotion speed of local officials and the COVID-19 confirmed cases.Results:Our study showed that the promotion speed of provincial governors and mayors significantly affected the number of confirmed cases(β=−11.615,P<0.01).There was a significant interaction between the promotion speeds of provincial governors and mayors(β=−2594.1,P<0.01),indicating that they had a coordinated effect on the pandemic control.Additionally,mayors with different promotion speeds made a significant difference in controlling the imported cases and those who promoted faster better controlled the imported cases(β=−0.841,P<0.01).Mayors with full-time postgraduate education,titles,and majors in science and engineering had a better effect on controlling the number of confirmed cases.Conclusions:Our study provides evidence that the official accountability system has played an important role in containing the pandemic,which suggests that local officials motivated by the accountability system would respond to the pandemic actively for higher utility.Furthermore,provincial governors and mayors have played a coordinated effect in pandemic control.The above evidences reveal that implementing the official accountability system could improve the government’s emergency management capability and the efficiency of pandemic control.Therefore,adopting a strict accountability system could be effective in pandemic containment globally,especially in centralized countries.

A Comparative Study on Chemical Composition of Supercritical CO_2 Extraction Products in Peels of Trichosanthes kirilowii Maxim. from Changqing District

[Objective] This study aimed to investigate the differences in chemical composition of supercritical CO2 extraction products in peels of Trichosanthes kirilowii Maxim. from Changqing district. [Method] Supercritical fluidextraction （SFE） and GCMS method were applied to determine and analyze the chemical components of the extracts in peels of three strains of Trichosanthes kirilowii Maxim. [Result] The chemical components of supercritical CO2 extraction products in peels of three strains of Trichosanthes kirilowii Maxim. varied., and the number of chemical components with normalized percentage content higher than 1% was 5, 7 and 8, respectively. There are 14 kinds of common components, and the relative content of hexadecanoic acid was the highest. [Conclusion] Supercritical CO2 extracts in peels of different strains of Trichosanthes kirilowii Maxim. contain different chemical components, providing scientific basis for breeding excellent varieties and the development and utilization of Trichosanthes kirilowii Maxim.

基于EM-KF算法的微地震信号去噪方法

针对微地震信号能量较弱,噪声较强,使微地震弱信号难以提取问题,提出了一种基于EM-KF(Expectation Maximization Kalman Filter)的微地震信号去噪方法。通过建立一个符合微地震信号规律的状态空间模型,并利用EM(Expectation Maximization)算法获取卡尔曼滤波的参数最优解,结合卡尔曼滤波,可以有效地提升微地震信号的信噪比,同时保留有效信号。通过合成和真实数据实验结果表明,与传统的小波滤波和卡尔曼滤波相比,该方法具有更高的效率和更好的精度。

不同EMS处理时间对大豆诱变后代主要品质含量变化影响分析

为探究不同EMS处理时间对大豆诱变后代主要品质含量变化的影响,文章利用0.5%EMS溶液处理大豆高蛋白品系公交1057-1,分别浸泡4、7和10 h(清水冲洗处理为对照),对播种后获得的连续4代诱变后代主要品质含量变化进行分析。结果表明,与对照相较,经EMS处理后的植株成活率随处理时间延长而降低。其中,经EMS-4处理后的植株成活率最高,为43.5%;经EMS-10处理后的植株成活率最低,仅为16.0%。不同EMS处理时间对大豆诱变后代的蛋白质含量影响显著,对脂肪含量影响不明显。其中经EMS-4处理的诱变后代蛋白质含量高于其他3组处理;经EMS-4处理后的M2代蛋白质含量发生显著分离,并出现超亲个体,脂肪含量变化不显著。

甲基磺酸乙酯(EMS)对谷子种子及幼苗活力的影响

为了探明甲基磺酸乙酯(EMS)诱变嫩选18种子构建突变体库的最适条件,加快品种选育进程,本研究对嫩选18种子进行了3种处理时间、3种EMS处理浓度的化学诱导,并对M1种子发芽及幼苗生长的诱变效应进行研究。结果表明:与对照相比,EMS处理在浓度0.4%下浸种6 h对嫩选18的发芽率及幼苗生长影响差异不显著,随着EMS浸种浓度、时间增加,种子萌发及幼苗生长逐渐受到抑制,在1.2%浓度下浸种14 h对种子萌发及幼苗生长抑制作用最强,极大降低了嫩选18的发芽势、发芽率、成苗率,并且导致幼苗生长缓慢。以存活率达到50%为标准,通过回归方程计算出在6 h、10 h、14 h浸种时间下,EMS浓度的半致死剂量分别为0.99%、0.72%、0.38%,可作为嫩选18 EMS诱变建立突变体库的适合条件。

Chemical Constituents of Rheum tanguticum Maxim.ex Balf.

Aim To study the chemical constituents of the root and rhizome of Rheum tanguticum Maxim. ex Balf. Methods Chemical constituents were isolated and purified by many chromatographic methods, and their structures were elucidated by MS, NMR, and others. Results Twenty compounds were isolated and their structures were identified as β-sitosterol, chrysophanol, aloe-emodin, physcion, rhein, emodin, etc. Conclusion Among these compounds, 4-(4′-hydroxyphenyl)-2-butanone, 4-(4′-hydroxyphenyl)-2-butanone-4′-O-β-D-(2″-...