基于ASP+ACCESS的学生诚信等级评价系统设计与实现

随着高校教育行业的发展,传统的高校学生档案管理方式难以准确地记录学生的信用状况,建立科学、合理的学生诚信等级评价系统成为目前研究的重要方向。使用ASP技术作为系统开发工具,结合ACCESS后台数据库,对系统进行了需求分析,设计了系统的软件体系结构、功能模块和数据库,并把模糊综合评估法应用到学生诚信等级评定研究中,快速、高效、准确地对学生诚信等级进行评定,进一步方便了学校对学生诚信做出客观、合理的评价。

Optimization of MAC algorithm based on IEEE 802.15.4 in indoor positioning system

The mobility of the targets asks for high requirements of the locating speed in indoor positioning systems.The standard medium access control(MAC)algorithm will often cause lots of packet conflicts and high transmission delay if multiple users communicate with one beacon at the same time,which will severely limit the speed of the system.Therefore,an optimized MAC algorithm is proposed based on channel reservation to enable users to reserve beacons.A frame threshold is set to ensure the users with shorter data frames do not depend on the reservation mechanism,and multiple users can achieve packets switching with relative beacon in a fixed sequence by using frequency division multiplexing technology.The simulation results show that the optimized MAC algorithm proposed in this paper can improve the positioning speed significantly while maintaining the positioning accuracy.Moreover,the positioning accuracy can be increased to a certain extent if more channel resources can be obtained,so as to provide effective technical support for the location and tracking applications of indoor moving targets.

RSSI-based Algorithm for Indoor Localization

Wireless node localization is one of the key technologies for wireless sensor networks. Outdoor localization can use GPS, AGPS (Assisted Global Positioning System) [6], but in buildings like supermarkets and underground parking, the accuracy of GPS and even AGPS will be greatly reduced. Since Indoor localization requests higher accuracy, using GPS or AGPS for indoor localization is not feasible in the current view. RSSI-based trilateral localization algorithm, due to its low cost, no additional hardware support, and easy-understanding, it becomes the mainstream localization algorithm in wireless sensor networks. With the development of wireless sensor networks and smart devices, the number of WIFI access point in these buildings is increasing, as long as a mobile smart device can detect three or three more known WIFI hotspots’ positions, it would be relatively easy to realize self-localization (Usually WIFI access points locations are fixed). The key problem is that the RSSI value is relatively vulnerable to the influence of the physical environment, causing large calculation error in RSSI-based localization algorithm. The paper proposes an improved RSSI-based algorithm, the experimental results show that compared with original RSSI-based localization algorithms the algorithm improves the localization accuracy and reduces the deviation.

Heuristic based data scheduling algorithm for OFDMA wireless network

A system model based on joint layer mechanism is formulated for optimal data scheduling over fixed point-to-point links in OFDMA ad-hoc wireless networks. A distributed scheduling algorithm （DSA） for system model optimization is proposed that combines the randomly chosen subcarrier according to the channel condition of local subcarriers with link power control to limit interference caused by the reuse of subcarrier among links. For the global fairness improvement of algorithms, a global power control scheduling algorithm （GPCSA） based on the proposed DSA is presented and dynamically allocates global power according to difference between average carrier-noise-ratio of selected local links and system link protection ratio. Simulation results demonstrate that the proposed algorithms achieve better efficiency and fairness compared with other existing algorithms.

New strategies for collision resolution of multi-access channel

The truncated binary exponential back-off algorithm is one of the most effective methods applied in collision resolution process of random multi-access channel.In this study,two new strategies are presented to improve the capability of the truncated binary exponential back-off algorithm.In the new strategies,the sizes of the initial window size or the operating window sizes are adjusted dynamically,which always bring a significant improvement for the self-adaptability of the original algorithm.A series of experiments are simulated and the results verify that the new strategies can make the implementation more stable and effective than the original algorithm.

Sparse Code Multiple Access-Towards Massive Connectivity and Low Latency 5G Communications

Sparse code multiple access(SCMA) is a novel non-orthogonal multiple access technology considered as a key component in 5G air interface design. In SCMA, the incoming bits are directly mapped to multi-dimensional constellation vectors known as SCMA codewords, which are then mapped onto blocks of physical resource elements in a sparse manner. The number of codewords that can be non-orthogonally multiplexed in each SCMA block is much larger than the number of resource elements therein, so the system is overloaded and can support larger number of users. The joint optimization of multi-dimensional modulation and low density spreading in SCMA codebook design ensures the SCMA receiver to recover the coded bits with high reliability and low complexity. The flexibility in design and the robustness in performance further prove SCMA to be a promising technology to meet the 5G communication demands such as massive connectivity and low latency transmissions.

无蜂窝大规模MIMO系统接入点动态选择算法

在无蜂窝大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统中,所有接入点(Access Point,AP)为用户提供服务的方式会造成较大的功率消耗和增加前传链路压力。以“用户为中心”的AP选择方案为每个用户选择最佳的AP集合,使系统频谱效率和能量效率得以提升。为了给每个用户选择最佳的AP集合,提出了一种基于反向传播(Back Propagation,BP)神经网络的接入点动态选择算法。将AP与用户之间的大尺度衰落向量作为输入,AP与用户之间的连接关系作为BP神经网络输出的预测向量,同时,针对BP神经网络初始化权值,提出了一种改进的自适应遗传算法以提高算法的收敛速度和收敛性能。仿真结果表明,改进后的BP神经网络预测结果更接近于实际值,并且比全连接和启发式的AP选择方案具有更高的系统和速率。

An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facial deflection of about 30° on either side from the front face showed an average face recognition precision of 72.25%-81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.

改进PBFT算法的配电物联网接入认证方法

随着物联网与配电网深度融合,海量终端设备接入系统给配电物联网安全稳定运行带来巨大挑战。针对传统身份认证方式过于中心化且无法承载大规模终端等现状,设计一种基于区块链共识机制的配电物联网终端接入认证方法。由配电物联网网关负责对待接入终端节点登记注册,采用共识算法进行分布式认证,将合法终端上链存储。在传统PBFT算法的基础上,设计了配电物联网终端共识算法。该算法引入权重机制,根据终端节点权重选取认证节点,缩小共识规模;引入可验证随机函数增强主节点安全,避免启动视图切换协议,提高共识效率;结合实际应用场景优化一致性协议,降低通信开销。实验分析表明该方法可有效规避多种网络攻击,通信开销和吞吐量优于其他方法,系统抗攻击性较强,满足配电物联网对认证效率和系统可靠性等要求。

A Robust Wi-Fi Fingerprinting Indoor Localization Coping with Access Point Movement

A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental environmental changes,such as AP movement. In order to address this problem,a robust fingerprinting indoor localization method is initiated. In the offline phase,three attributes of Received Signal Strength Indication( RSSI) —average,standard deviation and AP's response rate—are computed to prepare for the subsequent computation. In this way,the underlying location-relevant information can be captured comprehensively. Then in the online phase, a three-step voting scheme-based decision mechanism is demonstrated, detecting and eliminating the part of AP where the signals measured are severely distorted by AP 's movement. In the following localization step,in order to achieve accuracy and efficiency simultaneously,a novel fingerprinting localization algorithm is applied. Bhattacharyya distance is utilized to measure the RSSI distribution distance,thus realizing the optimization of MAximum Overlapping algorithm( MAO). Finally,experimental results are displayed,which demonstrate the effectiveness of our proposed methods in eliminating outliers and attaining relatively higher localization accuracy.

基于属性加密的联盟区块链数据共享方法

在保证区块链内数据安全的前提下,数据共享的速度通常较慢,为减少数据共享所需时间,基于属性加密算法设计联盟区块链数据共享方法。构建分层访问树,在无密钥托管模式中完成对用户代理加密的分类,使用哈希函数简单处理分层模型;基于属性加密构造数据访问共享模型,定义数据库域值,设置系统公共参数,建立主密钥与公密钥,完成集中式的数据共享管理;设计区块链数据共享算法,依据公钥格式生成用户私钥,完成区块链内的数据共享。分别计算数据共享过程中安全索引、陷门生成、关键字搜索三个步骤的用时,由数据可知,在安全索引与关键字搜索时,属性加密算法的运算效率更高,在陷门生成过程中,属性加密算法的运算效率相对较高。该方法的数据共享总用时为991 ms,共享时间更短,速度更快。

基于ICAE算法的城市群高速公路网布局优化研究

为提升城市群高速公路网通达程度和可达性水平,建立了体现城市群特征的优化模型,并改进了帝国竞争算法,以哈长城市群为案例,探究布局优化效果。采用高速公路出行时间确定城市群可达性等级,通过考虑可达性、距离等参数建立优化模型;引入进化算子改进算法,构建进化帝国竞争算法;根据哈长城市群特征确定约束条件并求解,利用求解节点和周边道路网确定优化方案。研究表明:改进的算法收敛精度提高16.4%,时间缩短42.4%,共求解出6个节点,规划新建高速公路6条,改建高速公路14条,优化后的城市群高速公路2 h交通圈覆盖范围达到65.3%,14个县域交通圈等级上升,优化方案扩大了城市群2 h交通圈范围。

基于对称加密算法的嵌入式物联网终端访问认证方法

目前常规的嵌入式物联网终端认证方法主要通过对认证节点进行权重分配,实现认证节点安全等级分类,由于对认证请求命令的加密程度较弱,导致方法的安全性较差。对此,提出基于对称加密算法的嵌入式物联网终端访问认证方法。调整安全认证网络覆盖扩展机制,对认证网络拓扑结构进行优化;并采用对称加密算法生成密钥,对密钥系数进行优化调整,对终端访问接入信息进行认证。在实验中,对提出的方法进行了认证安全性的检验。经实验结果表明,采用提出的方法对进行终端访问认证时,认证方法被成功攻击的概率明显降低,具备较为理想的认证安全性。

生成式人工智能算法的法律风险及其规范化防控

生成式人工智能为代表的新型科技的发展,迅速席卷了数字社会的每个角落,在数字营销新业态中数据和算法的深度融合深刻改变了传统营销模式。但生成式人工智能算法权力的逐渐失控却导致了消费者法律主体地位异化、个性化推荐算法歧视异化、算法黑箱形成信息茧房异化等一系列法律风险。这些算法法律风险使得在数字营销新业态中算法信任逐步丧失。只有重构算法信任和建构算法问责机制才能化解算法危机和算法风险,从外部的责任归属上建构算法规范的治理信任,从内部的契约诚信建构算法规范的合约信任,从底层技术设计上建构算法规范的技术信任。在重构算法信任和算法问责的基础之上规制算法,重塑法律层面的主体—行为—责任链条,赋予消费者数据访问权平衡数据流通和保护,通过区块链技术从技术层面治理算法,以此实现数字营销新业态中生成式人工智能算法风险的法治化防控。

基于NOMA-VLC系统的GWO-DNN信道估计方法

为了解决室内非正交多址(NOMA)可见光通信(VLC)信道估计方法的可靠性和用户公平性低问题,提出了一种在NOMA-VLC系统中使用灰狼优化(GWO)算法对深度神经网络(DNN)进行信号补偿和恢复的方法,即GWO-DNN信道估计方法。该方法引入非线性收敛因子和二维混沌映射,有效提升了系统可靠性和多用户传输公平性。实验结果表明:在误码率(BER)为10^(-4)时,所提方法比最小均方误差(MMSE)方法最高可获得4.3 dB的信噪比(SNR)增益,两用户的SNR差异由3.2 dB降到0.9 dB;在不同阶次的正交幅度调制下,所提方法的性能均优于MMSE方法,且调制阶次越高性能改善越明显。

四川省产前筛查和产前诊断服务的空间可及性评估及优化对策

目的:在生育率下降、高危孕妇增加、优生优育意识提升的背景下,产前筛查和诊断的重要性和关注度快速提升,本研究立足四川省“卫生健康十大行动”要求,对四川省现有产前筛查和诊断服务的空间可及性进行精准评估,并提出优化建议,为政府决策提供有益的参考。方法:基于最短路径法评估分层级、分阶段的产前筛查和诊断服务空间可及性,分别以最少新增机构(产前筛查机构)和最短总旅行时间(产前诊断机构)为优化目标采用集合覆盖模型和Location-Allocation模型提出空间优化布局建议。结果:四川省产前筛查服务空间可及性较好,91.30%的育龄妇女在120 min覆盖范围内,仅宜宾市、泸州市和甘孜州、阿坝州、凉山州没有达到“行动”要求,增加12所产前筛查机构即可使所有市州的产前筛查服务达到“行动”目标,目前产前筛查机构到达产前诊断机构的平均驾车时间为49.51 min,通过改变7所产前诊断机构的位置即可实现包括新增筛查机构在内的总体交通时间最小化,原有78所产前筛查机构的平均用时将缩短至29.14 min。结论:“行动”目标可以通过合理投入资源高效达成,在实际配置过程中人力资源是最大的阻碍,需要政府部门对本研究提出的优化清单加大支持,本研究提出的评价方法和优化策略可用于指导健康中国相关目标的实现。

考虑最大通信量的物联网群体访问路由算法

针对因物联网群体用户访问数据量较大、访问频率不稳定,导致访问时延大、效率低的问题,提出一种基于事件驱动的访问路由算法予以解决。考虑到物联网群体数据动态特性强、难捕捉的问题,分析相邻访问节点位置信息、访问频率以及能耗变化与传输时延的关系。设置任意节点的簇首参数,通过物联网群体访问节点拓扑模型,计算簇首到访问点可容纳的最大通信量,作为路由算法的初始参照,采用事件驱动法将访问过程看作事件自然变动过程,结合实际负载情况,得到最终路由决策结果。实验结果表明,无论是干扰环境还是无干扰环境,所提路由算法的访问成功率均较高,且访问时延较低,具有一定的实用价值。

基于阈值和区间筛选的SCMA多用户检测算法

在5G通信用户信息高吞吐量和大规模连接应用场景下,稀疏多址接入(sparse code multiple access,SCMA)可用于改善5G智慧场景通信质量。为了降低SCMA系统检测端算法复杂度,基于Max-log-MPA算法,提出了一种基于单阈值门限和区间范围的多参数组合设定的消息传递算法。在用户信息值迭代运算前利用门限阈值进行判断,当满足设定条件后方可参与算法的迭代过程,迭代计算过程中再次设定区间参数进行过滤筛选,使得通信场景所需的用户节点概率值参与计算,用户信息得到译码,2部分参数均可一定程度筛选用户信息值,从而达到降低算法复杂度的目的。仿真结果表明,提出的算法收敛速度和算法复杂度明显优于消息传递算法(message passing algorithm,MPA),误比特率(bit error ratio,BER)性能损失也可得到保证,在误码率性能和算法复杂度权衡之间有灵活的参数组合方案,具有很强的适应性。

基于挖掘算法的用户大数据周期智能推荐仿真

随着互联网技术的快速发展,用户数量日益增长,社交网络平台对周期性智能推荐的需求也日益增加。为了解决当前智能推荐算法准确率低、推荐速度慢等问题,提出了一种基于挖掘算法的用户大数据周期智能推荐算法。算法首先采用协同推荐算法对用户历史行为进行分析,并通过数据相似性衡量智能推荐的效果,使用Top-N算法优化推荐过程,达到周期智能推荐的目的;然后采用基于神经网络的挖掘算法对智能推荐算法进行优化,挖掘长时数据关系的同时保持短时数据之间的非线性;最后通过引入灰色均衡算法对相似度计算优化,从而缩短推荐时间。实验结果表明,所提算法在相似度计算准确度方面提升7%,推荐精确度提升6%,召回率提升8%,有效地提高了数据周期智能推荐的精读和效率,提高了个性化服务的质量。

城市复杂环境下蜂窝接入式无人机多层次路径规划方法

近年来蜂窝接入式无人机在解决传统无人机模型的局限性方面被广泛应用,但还未形成有效应对城市多建筑群及突发障碍物环境下的路径规划方法。本文提出一种新的蜂窝式无人机多层次路径规划方法,由连通性感知的全局规划层和无碰撞的局部规划层组成,全局规划以地面基站信号覆盖范围为规划单元,基于图论算法求解最优宏观路径,以提高求解效率,局部规划侧重飞行避障,以改进的DQN算法求解最优无碰撞飞行路径。仿真实验表明,相较于传统方法,该方法能将飞行距离有效缩短16.02%,并将求解效率提升76.6%,满足复杂城市环境下的无人机寻路需求,为复杂城市环境中的无人机路径规划提供了一种有效的解决方案。