Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire network.Nevertheless,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack straightforwardly.Furthermore,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been detected.However,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure problem.Moreover,recently the high frequencies of DDoS attacks have increased dramatically.In this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has proposed.Our proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS occurrence.The obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

深度学习重建联合Smart去金属伪影算法在口腔金属植入物患者头颈CT血管成像中的应用

目的:探讨深度学习重建(DLR)联合Smart去金属伪影(MAR)算法在口腔金属植入物患者头颈CT血管成像(CTA)中的应用价值。方法:选择郑州大学第一附属医院2023年2月至6月口腔有不可拆卸金属植入物行头颈CTA的患者70例,采用以下3种方法重建图像:基于混合模型的自适应迭代重建(ASIR-V)50%算法(IR),ASIR-V50%联合Smart MAR算法(IR-S),高水平DLR联合Smart MAR算法(DLR-S)。测量不受伪影影响的颈内动脉C1段和头夹肌感兴趣区CT值的标准差(SD)2和SD4,作为图像噪声指标;计算颈内动脉C1段和舌部的金属伪影指数(AI)1和AI2;对颈内动脉C1段和口腔整体图像质量进行主观评分。结果:与IR组和IR-S组比较,DLR-S组SD2和SD4降低(P<0.05)。与IR组比较,IR-S组和DLR-S组AI1、AI2降低;与IR-S组比较,DLR-S组AI1、AT2降低(P<0.05)。与IR组比较,IR-S组和DLR-S组口腔整体和颈内动脉C1段图像质量主观评分均增高;与IR-S组比较,DLR-S组图像质量主观评分增高(P<0.05),9例患者舌部可见新的伪影。结论:Smart MAR联合DLR可减少口腔植入物造成的金属伪影,提高头颈CTA图像质量。但Smart MAR可能引入新的伪影,需联合未加入Smart MAR的图像进行分析。

A Study on Service-Oriented Smart Medical Systems Combined with Key Algorithms in the IoT Environment

A smart medical service system architecture is proposed in this paper to increase medical resource utilization and improve the efficiency of the medical diagnosis process for complex business scenarios in the Medical Internet of Things(MIoT)environment.The resource representation model theory,multi-terminal aggregation algorithm,and the resource discovery algorithm based on latent factor model are also studied.A smart medical service system within the IoT environment is then developed,based on the open source project.Experimental results using real-world datasets illustrate that the proposed smart medical service system architecture can promote the intelligent and efficient management of medical resources to an extent,and assists in the develop towards digitization,intelligence,and precision in the field of medicine.

A Genetic Algorithm-Based Smart Antenna Technique for Anti-Collision of Multiple SAW ID-Tags

In the radio frequency identification (RFID) system based on surface acoustic wave (SAW) technique, some tags often locate in the field of a transceiver at the same time. These tags produce simultaneous echo signals which "collide" when they arrive back at the transceiver, which leads to difficult identification. In this paper, smart antenna technique is presented to implement anti-collision in SAW RFID system. The direction of arrivals (DOAs) are used to denote the locations of tags, and genetic algorithm (GA) is suggested to find the optimal estimates of the DOAs in complex multimodal search spaces. Once the DOAs are obtained, the array weights are formed and the signals of tags are recovered to implement decoding. The experimental results show that the GA-based smart antenna technique works well in some occasions.

Mathematical Model and Algorithm for the Task Allocation Problem of Robots in the Smart Warehouse

In the smart warehousing system adopting cargo-to-person mode, all the items are stored in the movable shelves. There are some warehouse robots transporting the shelves to the working platforms for completing order picking or items replenishment tasks. When the number of robots is insufficient, the task allocation problem of robots is an important issue in designing the warehousing system. In this paper, the task allocation problem of insufficient warehouse robots (TAPIR) is investigated. Firstly, the TAPIR problem is decomposed into three sub-problems: task grouping problem, task scheduling problem and task balanced allocation problem. Then three sub-problems are respectively formulated into integer programming models, and the corresponding heuristic algorithms for solving three sub-problems are designed. Finally, the simulation and analysis are done on the real data of online bookstore. Simulation results show that the mathematical models and algorithms of this paper can provide a theoretical basis for solving the TAPIR problem.

基于smart AGENT的纺织企业竞争情报智能采集方法

对纺织企业竞争情报归属权离散、时空离散、价值离散等特点及分布现状进行了系统的分析,提出应用smartAGENT进行编程,达到快速有效的internet定位,实现使用者快速找到特定的纺织企业竞争情报信息地址,获取相关的纺织企业竞争情报信息.

Smart Help的研究与开发

帮助文件是系统软件及应用软件的重要组成部分，目前比较流行的 Ｗｉｎｄｏｗｓ 帮助系统仍存在一些弱点，这就是Ｓｍａｒｔ Ｈｅｌｐ 系统开发的原因。本文简单地探讨Ｓｍａｒｔ Ｈｅｌｐ 开发中句子语义的处理问题。

Parallel Building: A Complex System Approach for Smart Building Energy Management

These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i.e., artificial systems,computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.

Automatic Test Data Generation for Java Card Applications Using Genetic Algorithm

The main objective of software testing is to have the highest likelihood of finding the most faults with a minimum amount of time and effort. Genetic Algorithm (GA) has been successfully used by researchers in software testing to automatically generate test data. In this paper, a GA is applied using branch coverage criterion to generate the least possible set of test data to test JSC applications. Results show that applying GA achieves better performance in terms of average number of test data?generations, execution time, and percentage of branch coverage.

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.

智慧农业下的水肥一体机精准化作业研究

针对我国农业水资源和化肥资源的利用率较低的问题,在智慧农业下对水肥一体机的精准化作业进行了研究。水肥一体机的主要组成包括控制系统、监测系统、灌溉施肥系统、网络传输系统和数据中心。将粒子群算法与PID控制算法结合,对粒子群-模糊PID控制器进行设计,并对水肥溶液的EC值和pH值进行控制,提高了水肥一体机的控制精度,减少了控制时间。为了验证水肥一体机的性能,进行了数据采集准确性分析试验和精准化作业控制试验,结果表明:数据采集较为准确,且可以精准地配比水肥溶液,满足作物的灌溉要求。

Optimal Demand-Side Management for Smart Micro Grid with Storage

In this paper, an autonomous and distributive demand-side management based on Bayesian game theory is developed and applied among users in a grid connected micro-grid with storage. To derive that strategy, an energy consumption of shiftable loads belonging to a given user is modelled as a noncooperative three-player game of incomplete information, in which each user plays against the storage unit and an opponent gathering all the other users in the micro-grid. Each player is assumed to be endowed with statistical information about its behavior and that of its opponents so that he can take actions maximizing his expected utility. Results of the proposed strategy evaluated by simulating, under MATLAB environment, a connected micro-grid with storage device evidence its efficacy when employed to manage the charging of electric vehicles.

人工智能辅助司法审判:现实阻滞与法治策略

随着人工智能技术的飞速发展,人工智能系统逐渐被引入司法审判领域,在辅助司法审判应用过程中发挥着“连接桥”“参考书”“预警器”作用。但限于科技发展的渐进性,当前人工智能辅助司法审判的功能发挥仍面临诸多困境,主要表现为人工智能的技术局限化、审判法官的应对失能化以及科技公司的生产机械化等。为破解数字司法的实践难题,首先,应当突破传统技术限制,完善对先进审判系统的宏观设计;其次,应当设置体系化技术培训方案,切实提高应用主体系统操作能力;最后,应当从严把握技术准入标准,规范产品提供行为,为人工智能辅助司法审判的应用提供强有力的保障。

数据、算力和算法结合反映新质生产力的数字化发展水准

数字经济的发展促进新的劳动主体、新的生产工具和新的生产要素不断涌现,为人们认识世界和改造世界创造了新模式,新质生产力的数字化发展是新质生产力在数字经济领域的表现。数字经济时代,数据作为新质生产力数字化发展的新要素,算力体现新质生产力数字化发展的新动能,算法反映新质生产力数字化发展的新优势,数据、算力和算法的结合反映了新质生产力的数字化发展水准,形成数字时代的新质生产力。新发展阶段,数据、算力和算法的结合首先引起生产力的决策革命,其次引起生产力的工具革命、劳动力革命、生产要素革命和技术—经济范式革命,进一步推动新质生产力的数字化发展。在全球经济新周期的背景下,提高新质生产力的数字化发展水平已经成为推动经济社会高质量发展的关键力量。

基于波束形成的海上智能天线算法

远距离海上雷达探测由于海上环境的复杂性容易受到杂波干扰,常用的最小均方算法通过在干扰噪声中形成主波束并引导至目标信号,有效解决了杂波干扰问题。通过对自适应智能天线波束形成技术的海上应用进行研究,给出了常用的天线阵列结构模型和信号模型,研究了智能天线中的自适应波束成型算法,目前在这一领域的解决方案中最小均方算法最为常见,但其存在收敛速度差、通用性低等问题。本文将优化遗传算法引入,对传统遗传算法中初始种群规划和交叉操作进行了优化和改进。仿真实验表明,与最小均方算法、传统遗传算法相比,优化遗传算法对杂波的抑制效果提高了28%,收敛速度提升了3倍,具有良好发展前景。

基于ARM处理器的温室大棚智能监控系统设计

为了实现温室大棚的智能监测与控制,设计基于ARM处理器的温室大棚智能监控系统。系统分为三个部分:数据采集及设备控制终端、智能网关终端、Android手机客户端,数据采集及设备控制终端以ARM微处理为核心,通过传感器、算法、Wi-Fi等技术,实现温室大棚数据采集、数据处理、数据传输及执行设备的智能控制,智能网关终端实现多个温室大棚数据从内网发送至公网,Android手机客户端实现数据的接收与发送。经测试验证,系统能够实现温室大棚的智能监控。

外卖智能派单的订单-骑手多目标匹配模型及其适应性算法研究

随着外卖业的快速发展,外卖智能派单成为外卖服务的重要形式。然而,目前相关研究侧重于研究外卖配送路径问题,不利于解决外卖智能派单的多目标动态决策中存在的问题。本文从外卖订单-骑手匹配的角度,分析了外卖平台智能派单的五个基本目标和四个基本约束,构建了多目标的订单-骑手匹配优化模型,进而给出了基于适应性策略的实时优化算法。最后,本文设计了外卖智能派单的仿真实验,对比了骑手-订单数量均衡、骑手供过于求、骑手供不应求等三种情况下的优化情况,验证了基于适应性策略的实时优化算法的优化效果。本研究对外卖平台发展智能派单模式具有较好的理论与实践价值。

AI算法时代智慧图书馆建设面临的机遇、困境及应对策略

文章通过分析AI算法的特点及其在智慧图书馆建设中的具体影响,总结出AI算法虽为智慧图书馆建设带来无限可能,但因其自主性和复杂性以及规范性治理不足,给智慧图书馆带来“知识”恐慌、信息茧房、预测决策偏差、读者隐私泄露等诸多困境,并从多方面剖析困境产生的原因,提出相应的解决措施:可以通过加强馆员自身算法素养、创设算法素养培育环境、负责任地选择和开发算法、完善读者隐私保护体系等策略,更好地发挥AI算法在智慧图书馆建设中的积极作用,克服其存在困境。

基于虚拟仿真技术的智能制造生产线自动控制系统

针对智能制造生产线中视觉分拣容易产生漏检和误检的问题,提出一种基于虚拟仿真技术的智能制造生产线自动控制系统。系统创新地使用云端PLC结合人工智能进行虚拟仿真,提高了自动控制的算力,使整体系统控制更加灵活可控。同时结合YOLOv5算法进行目标检测,在保证检测精确度的同时,进一步提高目标检测效率。经过实际仿真实验,设计的自动控制系统召回率、精确率以及平均精度均值等指标均符合实际生产需求,控制系统更加精确。

ZigBee无线通信技术在智能家居网络中的应用

ZigBee无线通信技术在智能家居组网中具有重要的应用。为了探究其使用场景和方法,文章首先分析了基于ZigBee的智能家居网络组网方式,其次介绍了接收信号强度指示(Received Signal Strength Indicator,RSSI)算法的测距原理和DV-Hop算法的测点原理,最后针对两种算法的局限性,提出基于阈值的改进定位算法。