An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

Recently, many researchers have used nature inspired metaheuristic algorithms due to their ability to perform optimally on complex problems. To solve problems in a simple way, in the recent era bat algorithm has become famous due to its high tendency towards convergence to the global optimum most of the time. But, still the standard bat with random walk has a problem of getting stuck in local minima. In order to solve this problem, this research proposed bat algorithm with levy flight random walk. Then, the proposed Bat with Levy flight algorithm is further hybridized with three different variants of ANN. The proposed BatLFBP is applied to the problem of insulin DNA sequence classification of healthy homosapien. For classification performance, the proposed models such as Bat levy flight Artificial Neural Network (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) are compared with the other state-of-the-art algorithms like Bat Artificial Neural Network (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distribution back propagation (BatGDBP), in-terms of means squared error (MSE) and accuracy. From the perspective of simulations results, it is show that the proposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185, and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on WL5. While on WL10 the proposed BatLFANN achieved 99.89899% accuracy with MSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of 0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853% accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracy with MSE of 0.006738 which achieve better accuracy as compared to the other hybrid models.

     

语音编码算法的神经网络增益滤波器

增益-波形乘积码书结构广泛用于 CELP 语音编码算法, 它们使用 Levinson-Durbin(L-D)方法更新增益滤波器系数. 本文对 BP 神经网络算法与 L-D 方法进行了比较. 用 BP 神经网络增益滤波器进行语音编码, 其计算量仅为 G.728 的 L-D 方法的 6.7%, 但平均分段 SNR 高出 G.728 算法 0.156 dB. 同时, 用 BP 神经网络算法评价了 16 和 20 样点激励矢量增益滤波器, 效果同样很好. 但是, 由于考察增益预测器时量化器还不存在, 因此无法用量化信噪比评价滤波器性能. 本文提出一种信噪比估计方法, 可使增益预测器的优化与量化问题分开处理. 实验表明用这种信噪比估计方法选择增益滤波器十分有效.     

Distribution of Link Distances in a Wireless Network

The probability distribution is found for the link distance between two randomly positioned mobile radios in a wireless network for two representative deployment scenarios: (1) the mobile locations are uniformly distributed over a rectangular area and (2) the x and y coordinates of the mobile locations have Gaussian distributions. It is shown that the shapes of the link distance distributions for these scenarios are very similar when the width of the rectangular area in the first scenario is taken to be about three times the standard deviation of the location distribution in the second scenario. Thus the choice of mobile location distribution is not critical, but can be selected for the convenience of other aspects of the analysis or simulation of the mobile system.     

An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks

With the emergence of 5G mobile multimedia services, end users’ demand for high-speed, low-latency mobile communication network access is increasing. Among them, the device-to-device (D2D) communication is one of the considerable technology. In D2D communication, the data does not need to be relayed and forwarded by the base station, but under the control of the base station, a direct local link is allowed between two adjacent mobile devices. This flexible communication mode reduces the processing bottlenecks and coverage blind spots of the base station, and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless networks. One of the important factors which affects the quality-of-service (QoS) of D2D communications is co-channel interference. In order to solve this problem of co-channel interference, this paper proposes a graph coloring based algorithm. The main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user resource. The proposed algorithm also provides simpler power control. The heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is acquired. Simulation results show that the proposed algorithm effectively reduced the co-channel interference, power consumption and improved the system throughput as compared with existing algorithms.     

网络媒体环境下的视觉传达设计探究

目的探索网络媒体环境下视觉传达设计的构成要素与发展策略。方法对比传统的视觉传达设计与网络媒体环境下视觉传达设计,探索两者的诸多不同之处,引出网络媒体环境下视觉传达设计所展现出的强大生命力,然后借助网络媒体的特征与其在视觉传达设计中表现出的自由、交互和实效等方面的突出优势,就其在页面布局、视觉流程、图像处理、色彩搭配等细节上的具体要求展开探索,并结合网络媒体环境下的页面设计和广告设计进行深入论述,探索这一大背景下视觉传达设计的未来发展。结论网络时代的发展促使网络媒体成为视觉传达的主要载体,这不仅带来了视觉传达设计的变化,丰富了设计风格、设计思维方式,还凭借神奇的个性风貌影响着人们的生活,满足了信息时代背景下人们对信息的现实需求。     

MPOA中流判别的神经网络方法

对MOPA中流的判别问题进行了探讨，首先对流判别时所应考虑的各种因素进行归纳，然后提出了基于Hopfield神经网络的判别方法，实验表明，该方法可有效地考滤流和ATM网络两方面的因素，最终结果令人满意。     

Perimeter-based coverage optimization to improve lifetime in wireless sensor networks

The most important problem in a Wireless Sensor Network (WSN) is to optimize the use of its limited energy provision, so that it can fulfil its monitoring task as long as possible. Among known available approaches that can be used to improve power management, lifetime coverage optimization provides activity scheduling which ensures sensing coverage while minimizing the energy cost. In this article an approach called Perimeter-based Coverage Optimization protocol (PeCO) is proposed. It is a hybrid of centralized and distributed methods: the region of interest is first subdivided into subregions and the protocol is then distributed among sensor nodes in each subregion. The novelty of the approach lies essentially in the formulation of a new mathematical optimization model based on the perimeter-coverage level to schedule sensors' activities. Extensive simulation experiments demonstrate that PeCO can offer longer lifetime coverage for WSNs compared to other protocols.     

Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs

In recent years, progressive developments have been observed in recent technologies and the production cost has been continuously decreasing. In such scenario, Internet of Things (IoT) network which is comprised of a set of Unmanned Aerial Vehicles (UAV), has received more attention from civilian to military applications. But network security poses a serious challenge to UAV networks whereas the intrusion detection system (IDS) is found to be an effective process to secure the UAV networks. Classical IDSs are not adequate to handle the latest computer networks that possess maximum bandwidth and data traffic. In order to improve the detection performance and reduce the false alarms generated by IDS, several researchers have employed Machine Learning (ML) and Deep Learning (DL) algorithms to address the intrusion detection problem. In this view, the current research article presents a deep reinforcement learning technique, optimized by Black Widow Optimization (DRL-BWO) algorithm, for UAV networks. In addition, DRL involves an improved reinforcement learning-based Deep Belief Network (DBN) for intrusion detection. For parameter optimization of DRL technique, BWO algorithm is applied. It helps in improving the intrusion detection performance of UAV networks. An extensive set of experimental analysis was performed to highlight the supremacy of the proposed model. From the simulation values, it is evident that the proposed method is appropriate as it attained high precision, recall, F-measure, and accuracy values such as 0.985, 0.993, 0.988, and 0.989 respectively.     

Reconfigurable Sensing Time in Cooperative Cognitive Network Using Machine Learning

A cognitive radio network (CRN) intelligently utilizes the available spectral resources by sensing and learning from the radio environment to maximize spectrum utilization. In CRNs, the secondary users (SUs) opportunistically access the primary users (PUs) spectrum. Therefore, unambiguous detection of the PU channel occupancy is the most critical aspect of the operations of CRNs. Cooperative spectrum sensing (CSS) is rated as the best choice for making reliable sensing decisions. This paper employs machine-learning tools to sense the PU channels reliably in CSS. The sensing parameters are reconfigured to maximize the spectrum utilization while reducing sensing error and cost with improved channel throughput. The fine-k-nearest neighbor algorithm (FKNN), employed in this paper, estimates the number of samples based on the nature of the channel under-specific detection and false alarm probability demands. The simulation results reveal that the sensing cost is suppressed by reducing the sensing time and exploiting the traditional fusion rules, validating the effectiveness of the proposed scheme. Furthermore, the global decision made at the fusion center (FC) based on the modified sensing samples, results low energy consumption, higher throughput, and improved detection with low error probabilities.     

计算机通信神经网络纠错的研究

为降低计算机通信中的误码率，提出把具有快速并行处理功能的神经网络纠错技术引入到计算机通信中。研制出具有自动纠错功能的计算机通信软件，该软件能够纠正每１６位二元码中不多于３位错的随机性误码和多于１３位，少于１６位错的突发性误码。     

Resource Allocation for Throughput Maximization in Cognitive Radio Network with NOMA

Spectrum resources are the precious and limited natural resources. In order to improve the utilization of spectrum resources and maximize the network throughput, this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonal multiple access (CRN-NOMA). NOMA, as the key technology of the fifth-generation communication (5G), can effectively increase the capacity of 5G networks. The optimization problem proposed in this paper aims to maximize the number of secondary users (SUs) accessing the system and the total throughput in the CRN-NOMA. Under the constraints of total power, minimum rate, interference and SINR, CRN-NOMA throughput is maximized by allocating optimal transmission power. First, for the situation of multiple sub-users, an adaptive optimization method is proposed to reduce the complexity of the optimization solution. Secondly, for the optimization problem of nonlinear programming, a maximization throughput optimization algorithm based on Chebyshev and convex (MTCC) for CRN-NOMA is proposed, which converts multi-objective optimization problem into single-objective optimization problem to solve. At the same time, the convergence and time complexity of the algorithm are verified. Theoretical analysis and simulation results show that the algorithm can effectively improve the system throughput. In terms of interference and throughput, the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access (OFDMA). This paper provides important insights for the research and application of NOMA in future communications.     

网上录取计算机网络系统的建立

根据网上录取的要求和流程,从系统的网络平台设计,服务器平台设计以及软件开发平台设计几方面阐述了建立网上招生信息系统模式的可能。     

全光放大波分复用环网中功率管理方案

提出了一种新颖的功率管理方案用于波分复用全光环网中,它以节点孤立原则为基础,采用预均衡技术对经EDFA放大后的所有光信道功率均衡,这样不但简化了网络监控方案,同时增强了网络监控功能,最后,把该功能管理方案应用到SHAONET全光通信环网试验平台中,计算机仿真结果与实验结果十分吻合,证明该环网功率管理方案是可行且有效的。     

WDM光网络中分级功率均衡方案

提出了一种新的分级光功率均衡的以及节点隔离原则,分析了该方法对系统接收机Q值的影响。结果表明,当复用段江功率水平整体波动时,选择合适的复用段级均衡实现方式,可以维持与仅用通道级均衡基本相同的能性,同时简化了调节过程,降低了对通道级均衡单元的要求。     

基于遗传算法优化BP神经网络的SCR脱硝系统催化剂体积设计

火电厂SCR脱硝系统的设计需要在满足脱硝效率的同时,尽可能节约成本,因此需要准确预测SCR脱硝所需的催化剂体积。火电厂的烟气条件复杂多变,烟气温度、烟气流量、出入口NO_x浓度等参数都会影响SCR催化剂的体积设计,因此催化剂体积预测是一个多因素耦合的问题。针对这一特点,使用BP神经网络对催化剂体积设计进行了预测,并针对该模型结构上的缺陷,进行基于遗传算法优化的神经网络建模研究。结果表明,遗传算法优化后的BP神经网络模型预测精度和数据拟合能力均有提高,为脱硝系统的催化剂体积设计提供了新思路。     

核电站穹顶内热环境的数值模拟

以CPR1000核电站R55区穹顶为研究对象,基于计算流体力学(CFD)技术,应用P-1辐射模型对穹顶内部温度场进行了3D稳态数值模拟与实验验证。结果表明:6月26日6个时刻,空穹顶内Z=11m处的温度模拟值与实测值吻合良好,最大相对误差为3.23%,平均相对误差为1.05%;9:00穹顶内垂直方向温度分布梯度明显,最大温差达到6℃。P-1辐射模型模拟太阳辐射对核电站穹顶内热环境的影响是可行的。     

论网络环境下思想政治教育方法的创新

新时期网络环境在各方面对我们所处的时代产生着深刻影响，亦对高校思想政治教育产生影响；高校思想政治教育的实效性及理想教育效果的获得，必定源干坚持不懈地对其方法论研究的与时俱进，教育手段的现代化追求，多层次、多视角的方法研究的执着；源于对“育人为本”教育理念的深刻认识。     

网络环境下标准馆服务模式的转变(英文)

In the network environment,changes of resources structure,information technology and users requirements of standards library cause series of problems such as unobvious effect of services,low efficiency...     

Water as a Modifier in a Hybrid Coordination Network Glass

Chemical diversification of hybrid organic–inorganic glasses remains limited, especially compared to traditional oxide glasses, for which property tuning is possible through addition of weakly bonded modifier cations. In this work, it is shown that water can depolymerize polyhedra with labile metal–ligand bonds in a cobalt-based coordination network, yielding a series of nonstoichiometric glasses. Calorimetric, spectroscopic, and simulation studies demonstrate that the added water molecules promote the breakage of network bonds and coordination number changes, leading to lower melting and glass transition temperatures. These structural changes modify the physical and chemical properties of the melt-quenched glass, with strong parallels to the “modifier” concept in oxides. It is shown that this approach also applies to other transition metal-based coordination networks, and it will thus enable diversification of hybrid glass chemistry, including nonstoichiometric glass compositions, tuning of properties, and a significant rise in the number of glass-forming hybrid systems by allowing them to melt before thermal decomposition.