音乐物联网的设计与研究

音乐物联网(Internet of musical things,IoMusT)是一个涉及多学科、多专业的综合工程,以往的研究都是单一的从某一个方面着手,缺乏一个统一的研究框架,完整的研究体系还不健全。研究重点是从工程的角度定义音乐事件,并结合当下互联网各个环节的技术特性,从工程的角度定义音乐事件,构造一个小范围的IoMusT生态系统,从软硬件方面测试IoMusT性能,进而验证IoMusT设计架构效。     

基于霍尼韦尔WEBs的智能照明监控实训系统设计与实现

从建筑智能化专业楼宇自控系统工程技术课程的实训教学实际需要出发,以模拟实际智能建筑现场为背景,开发了智能照明监控实训系统。该系统使用霍尼韦尔WEBs为系统软件平台,应用霍尼韦尔Spyder DDC控制器PUB6438S作为系统的控制核心,利用人体红外感应、定时时间和光照度感应等方法进行实时控制。学生利用该实训教学系统可以更加深刻理解楼控系统软硬件开发的整个生命周期,对楼控项目设计和编程调试等应用技术进行系统学习和实训,从而提升职业院校学生的工程实践能力。     

A path planning model based on nested regular hexagons using weighted centroid localization

In many wireless sensor network (WSN) applications, the location of a sensor node is crucial for determining where the event or situation of interest occurred. Therefore, localization is one of the critical challenges in WSNs. Mobile anchor node assisted localization (MANAL) is one of the promising solutions for the localization of statically deployed sensors. The main problem in MANAL localization is that the path planning of the mobile anchor (MA) node should be done so that the localization error in the network will be minimal and that all unknown nodes in the network are covered. This paper proposes a new path planning approach called nested hexagons curves (NHexCurves) for MANAL. NHexCurves guarantees that it will receive messages from at least three non-collinear anchors to locate all unknown nodes in the network. The proposed model has compared six different path planning schemes in the literature using weighted centroid localization (WCL). In these comparisons, first of all, localization errors of the models are compared using some statistical concepts. Second, the variation of the localization error according to parameters such as resolution (R) and the standard deviation of noise (σ) is observed. Then, with similar approaches, the standard deviation of errors, localization ratio, scalability performances, and finally, path lengths of the models are examined. The simulation results show that the NHexCurves static path planning model proposed in this study stands out compared to other models with high localization error and localization ratio performance, especially at low resolutions, due to its path design. At the same time, the lowest error values according to σ are obtained with the proposed model among all models considered.     

Stochastic Programming For Order Allocation And Production Planning

Stochastic demand is an important factor that heavily affects production planning. It influences activities such as purchasing, manufacturing, and selling, and quick adaption is required. In production planning, for reasons such as reducing costs and obtaining supplier discounts, many decisions must be made in the initial stage when demand has not been realized. The effects of non-optimal decisions will propagate to later stages, which can lead to losses due to overstocks or out-of-stocks. To find the optimal solutions for the initial and later stage regarding demand realization, this study proposes a stochastic two-stage linear programming model for a multi-supplier, multi-material, and multi-product purchasing and production planning process. The objective function is the expected total cost after two stages, and the results include detailed plans for purchasing and production in each demand scenario. Small-scale problems are solved through a deterministic equivalent transformation technique. To solve the problems in the large scale, an algorithm combining metaheuristic and sample average approximation is suggested. This algorithm can be implemented in parallel to utilize the power of the solver. The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given, then the problems of the first and second stages can be decomposed.     

Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

Recent advancements in cloud computing (CC) technologies signified that several distinct web services are presently developed and exist at the cloud data centre. Currently, web service composition gains maximum attention among researchers due to its significance in real-time applications. Quality of Service (QoS) aware service composition concerned regarding the election of candidate services with the maximization of the whole QoS. But these models have failed to handle the uncertainties of QoS. The resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by end-users. On the other hand, trip planning is an essential technique in supporting digital map services. It aims to determine a set of location based services (LBS) which cover all client intended activities quantified in the query. But the available web service composition solutions do not consider the complicated spatio-temporal features. For resolving this issue, this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model (F3L-WSCM) in a cloud environment for location awareness. The presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking, hotels, car rentals, etc. At the next stage, the firefly algorithm is applied to generate composition plans to minimize the number of composition plans. Followed by, the fuzzy subtractive clustering (FSC) will select the best composition plan from the available composite plans. Besides, the presented F3L-WSCM model involves four input QoS parameters namely service cost, service availability, service response time, and user rating. An extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy, execution time, and efficiency.     

室内场景下应用拓扑结构的高效路径规划算法

针对基于随机采样的路径规划算法效率低且采样具有随机性的问题,提出一种应用拓扑结构的高效路径规划算法ATIRRT^*。通过引入拓扑节点代替STIRRT*算法中Harris角点检测算法得到的特征点进行采样,给出基于阈值的自适应选择方法来消除路径骨架上提取的冗余特征点,利用该阈值得到的拓扑节点可以使随机树的扩展更具方向性,从而减少寻找初始路径的时间和代价。根据非单一父节点的连接方式加强交叉支路上的拓扑节点间的联系,通过节点扩充策略增加相邻拓扑节点间的节点数量以加快优化算法的收敛。在此基础上定义相关约束条件将初始路径分段并进行逐段优化,以提高优化算法的效率。在常规环境、狭长空间和仿真的室内环境3种类型地图上的仿真结果表明,相较于STIRRT^*算法,改进算法在规划路径长度上平均减少8%,在规划时间上平均降低10%,可快速地找到更优的初始路径,同时在优化过程中减少了无用的探索空间,提高了搜索效率。     

多周期家庭护理的路径规划与调度算法

在家庭护理服务质量方面,现有研究大多考虑老人的服务需求是否得到满足、服务是否及时、老人对服务是否满意等因素,忽略了老人在选择护工上的偏好习惯。提出带服务约束的多周期家庭护理路径规划与调度问题,并将其归约为多车场车辆路径优化问题,证明该问题的NP难解性。为了在老人提供的黑名单、必选服务技能、服务价格等约束下最优化家庭护理机构的服务质量,提出贪心算法,优先为服务开始时间早的老人提供服务,为老人安排最高服务质量的护工。以贪心算法求得的结果作为初始解,并针对该问题的多周期性质,定制一个遗传算法对初始解进行优化,以获得更优的多周期护工路径规划方案。在3组不同规模的开源数据集上进行实验,结果表明,与基准算法和随机算法相比,所提出的贪心算法将服务质量分别提高了31.7%和79.8%,定制的遗传算法将服务质量分别提高了65.7%和126.3%。     

基于改进型能量守恒SOC估算法的电动汽车三段式智能充电方式研究

为研究电动汽车安全、快速的智能充电方式,基于传统能量守恒法,考虑电池容量衰减和电池内阻损失对荷电状态(state of charge,SOC)估算的影响,提出改进型能量守恒SOC估算方法。对比分析几种传统充电方式,根据马斯定理确定最佳充电电流,提出以改进型能量守恒SOC估算法得到的SOC值作为判断依据的电动汽车三段式(小电流充电、脉冲充电、恒压充电)智能充电方式,并建立其仿真模型。结果表明:改进型能量守恒SOC估算法得到的SOC值要小于传统能量守恒法,其更加接近真实SOC值;三段式智能充电方式能根据电池组SOC值的变化智能地选择具体充电方式,实现了对电池的安全、快速充电。提出的基于改进型能量守恒SOC估算的三段式智能充电方式对当前电动汽车充电方式的研究提供了一定的参考价值,为智能充电方式研究效能的提升提供了一种可行方法,也为智能充电理论应用于工程实践打下基础。     

世界主要国家工程科技重大计划与前沿问题综述

本文梳理了近年来世界各主要国家工程科技领域的重大战略计划和措施,简要分析了当前工程科技主要领域发展状况、水平,对主要国家工程科技领域计划的目标、路径与内容进行了比较分析,描绘了工程科技计划关系图、聚焦点和前沿问题。     

卫生经济学应用于慢性病防控决策的战略研究

为提高当前慢性病防控体系的效率,更好地遏制慢性病的流行,保护公众健康,2015年中国工程院设立了"卫生经济学应用于慢性病防控决策的战略研究"重大咨询项目。课题组调研发现,作为一种重要的卫生决策工具,卫生经济学在慢性病防控决策过程中尚处于初级应用阶段。对卫生经济学的重要性认识不足,对卫生经济学的方法的掌握和应用能力有限,制约了其在慢性病防控决策中的应用,因此建议建立多元主体参与的卫生经济学研究力量,加强卫生经济学应用于慢性病防控决策的大数据积累及应用研究,提出将卫生经济学应用于我国慢性病防控决策的战略框架。