RFID Indoor positioning based on Semi-supervised Actor-Critic Co-training

For large-scale radio frequency identification ( RFID) indoor positioning system, the positioning scale isrelatively large, with less labeled data and more unlabeled data, and it is easily affected by multipath and whitenoise. An RFID positioning algorithm based on semi-supervised actor-critic co-training (SACC) was proposed tosolve this problem. In this research, the positioning is regarded as Markov decision-making process. Firstly, theactor-critic was combined with random actions and selects the unlabeled best received signal arrival intensity(RSSI) data by co-training of the semi-supervised. Secondly, the actor and the critic were updated by employingKronecker-factored approximation calculate (K-FAC) natural gradient. Finally, the target position was obtained byco-locating with labeled RSSI data and the selected unlabeled RSSI data. The proposed method reduced the cost ofindoor positioning significantly by decreasing the number of labeled data. Meanwhile, with the increase of thepositioning targets, the actor could quickly select unlabeled RSSI data and updates the location model. Experimentshows that, compared with other RFID indoor positioning algorithms, such as twin delayed deep deterministic policygradient (TD3), deep deterministic policy gradient (DDPG), and actor-critic using Kronecker-factored trustregion ( ACKTR), the proposed method decreased the average positioning error respectively by 50.226%,41.916%, and 25.004%. Meanwhile, the positioning stability was improved by 23.430%, 28.518%, and38.631%.     

基于改进型灰狼算法的RFID网络规划

随着物联网技术的飞速发展,射频识别(Radio Frequency Identification,RFID)系统因具有非接触、快速识别等优点而成为了解决物联网问题的首选方案。RFID网络规划问题要考虑多个目标,被证明是多目标优化的问题。群体智能(Swarm Intelligence,SI)算法在解决多目标优化问题方面得到了广泛的关注。文中提出了一种改进型灰狼算法(Improved Grey Wolf Optimizer,IGWO),利用高斯变异算子和惯性常量策略来实现RFID网络规划。通过建立优化模型,在满足标签100%覆盖率、部署更少的阅读器、避免信号干扰、消耗更少的功率4个目标的基础上,将所提算法与粒子群算法(Particle Swarm Optimization,PSO)、遗传算法(Genetic Algorithm,GA)、帝王蝶算法(Monarch Butterfly Algorithm,MMBO)进行了对比分析。实验结果表明,灰狼算法在RFID网络规划时表现更优异,在相同的实验环境下,相较于其他算法,IGWO的适应度值比GA提高了20.2%,比PSO提高了13.5%,比MMBO提高了9.66%;并且覆盖的标签数更多,可以更有效地求出最优化方案。     

基于改进型多目标樽海鞘群算法的RFID阅读器天线优化部署

随着射频识别(Radio Frequency Identification,RFID)技术的飞速发展,在各种特殊的环境下(如工厂、仓库、监狱等),对RFID阅读器天线优化部署的需求开始受到广泛关注.针对目前RFID阅读器天线部署中存在的部署难度大、约束条件多且不易找到最优解和Pareto前沿等问题,文中提出了一种基于改进型多目标樽海鞘群算法(Multi-obj ective Salp Swarm Algorithm,MSSA)的RFID阅读器天线优化部署方法.预先构建多目标RFID阅读器天线优化部署模型,设定优化目标;多目标樽海鞘群算法对RFID阅读器天线优化部署模型进行优化训练,引入分离算子以优化搜索能力,并通过迭代不断寻找满足条件的非支配解,构建满足条件的Pareto解集,其即为优化的结果.实验数据表明,MSSA算法求解时无需先验知识和设置加权系数,收敛速度快;在相同实验环境下,MSSA算法与带观察者机制的蝙蝠(BA-OM)算法、粒子群(PSO)算法、细菌觅食优化(MCBFO)算法相比,覆盖率分别提高了33％,28％,20％;与同类型的求Pareto解集的混合萤火虫(HMOFA)算法相比,MSSA算法的负载均衡提高了7.14％,经济效益提高了59.74％,阅读器干扰减少34.04％.     

基于单片机的中国式摔跤比赛计分器开发设计

中国式摔跤是中华民族特色体育项目,其在国内部分高校及单位试点推广过程中,需要一种简单适用的比赛计分器。单片机的体积小、功能多、价格便宜、可靠性好、目前广泛应用于轻量化测量系统。本设计按照中国式摔跤竞赛规则要求,基于AT89S52单片机开发了比赛计分软件,完成了相关的硬件设计,制作出比赛计分器。经模拟比赛测试,该比赛计分器可以替代人工计分计时,达到了设计目的。     

基于两步刻蚀工艺的锥形TSV制备方法

以硅通孔(TSV)为核心的2.5D/3D封装技术可以实现芯片之间的高速、低功耗和高带宽的信号传输。常见的垂直TSV的制造工艺复杂,容易造成填充缺陷。锥形TSV的侧壁倾斜,开口较大,有利于膜层沉积和铜电镀填充,可降低工艺难度和提高填充质量。在相对易于实现的刻蚀条件下制备了锥形TSV,并通过增加第二步刻蚀来改善锥形TSV形貌。成功制备了直径为10～40μm、孔口为喇叭形的锥形TSV。通过溅射膜层和铜电镀填充,成功实现了直径为15μm、深度为60μm的锥形TSV的连续膜层沉积和完全填充,验证了两步刻蚀工艺的可行性和锥形TSV在提高膜层质量和填充效果方面的优势。为未来高密度封装领域提供了一种新的TSV制备工艺,在降低成本的同时提高了2.5D/3D封装技术的性能。     

自然激励下某电动汽车白车身模态参数识别

为了研究电动汽车白车身的动态特性,提出了一种基于自然激励的结构模态参数识别方法。 利用此方法对某电动汽车 白车身进行了结构模态参数识别,得到了其前三阶结构模态参数。 并将利用该方法识别的结果和传统方法识别结果进行了对 比,发现固有频率最大误差为 1. 8%,阻尼比最大误差为 13%,振型基本一致,进而验证了方法的正确性。 之后利用识别的模态 参数结合电动汽车工作特性,对该白车身的动态特性进行了评价。 提出的方法不需要专用的激励设备,可应用于不易激励的大 型、重型结构模态参数识别。 模态参数识别的结果对电动汽车白车身动态特性设计具有一定的指导意义。     

不依赖于量化参数的视频编码控制优化技术

目前,混合式视频编码技术普遍采用基于量化参数的Lagrange乘子优化算法(QP-L)实现预测编码的率失真优化计算.文中则提出一种新的率失真优化算法Adapt-L,可以根据视频信号的率失真统计特性,自适应地调整Lagrange乘子的计算.实验及数据分析表明,在QP-L算法的Lagrange乘子和视频信号的率失真统计信息...     

Random mating mayfly algorithm for RFID network planning

In order to improve robustness and efficiency of the radio frequency identification (RFID) network, a random mating mayfly algorithm (RMMA) was proposed. Firstly, RMMA introduced the mechanism of random mating into the mayfly algorithm (MA), which improved the population diversity and enhanced the exploration ability of the algorithm in the early stage, and find a better solution to the RFID nework planning (RNP) problem. Secondly, in RNP, tags are usually placed near the boundaries of the working space, so the minimum boundary mutation strategy was proposed to make sure the mayflies which beyond the boundary can keep the original search direction, as to enhance the ability of searching near the boundary. Lastly, in order to measure the performance of RMMA, the algorithm is then benchmarked on three well -known classic test functions, and the results are verified by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), and MA. The results show that the RMMA algorithm is able to provide very competitive results compared to these well-known meta-heuristics, RMMA is also applied to solve RNP problems. The performance evaluation shows that RMMA achieves higher coverage than the other three algorithms. When the number of readers is the same, RMMA can obtain lower interference and get a better load balance in each instance compared with other algorithms. RMMA can also solve RNP problem stably and efficiently when the number and position of tags change over time.     

RFID network planning based on improved brain storm optimization algorithm

In order to improve the service quality of radio frequency identification (RFID) systems, multiple objectives should be comprehensively considered. An improved brain storm optimization algorithm GABSO, which incorporated adaptive learning operator and golden sine operator into the original brain storm optimization (BSO) algorithm, was proposed to solve the problem of RFID network planning (RNP). GABSO algorithm introduces learning operator and golden sine operator to achieve a balance between exploration and development. Based on GABSO algorithm, an optimization model is established to optimize the position of the reader. The GABSO algorithm was tested on the RFID model and dataset, and was compared with other methods. The GABSO algorithm's tag coverage was increased by 9.62% over the Cuckoo search (CS) algorithm, and 7.70% over BSO. The results show that the GABSO algorithm could be successfully applied to solve the problem of RNP.