考虑多样化需求的诊疗设备维护和患者调度联合优化研究

针对诊疗设备维护和住院患者候诊难以有效协调的问题,建立了联合优化模型。首先,假设设备具有离散的劣化状态,将设备的劣化过程建模为连续时间马尔可夫链;其次,考虑到患者对诊疗设备的不同功能频率需求,以及不同劣化状态对患者治疗时间、费用的影响,以患者就诊顺序、检查策略、修复策略为决策变量建立了设备维护和患者调度模型;最后,采用改进后的非支配排序遗传算法对多目标问题进行了求解。实验结果验证了设备维护与患者调度联合优化模型的有效性。改进后的算法提高了整体和局部的搜索能力,且具有鲁棒性。     

考虑疫情扩散传播风险的多目标拼车应急管理优化调度模型

为了解决在疫情背景下传统拼车出行模式面临的交互传染风险,对疫情背景下的拼车管理进行分析,提出了将低风险及潜在风险乘客分开服务的拼车策略。在进一步考虑拼车系统应兼顾的社会效益及服务质量的基础上,构建了在满足基本额定盈利及最小化等待时长的双目标拼车应急管理优化调度模型,并给出了求解该模型的组合模拟退火算法。通过具体算例对模型的有效性进行验证,结果表明模型能够较快收敛到一个稳定值,且计算结果能够反映实际场景,说明了模型及算法的有效性。最后,通过一个仿真实验对所构建模型在病毒传播中的作用进行了分析,结果显示所构建的模型可以有效抑制拼车导致的病毒传播速度。     

Forest park landscape pattern system based on FPGA and GIS system

Perceptual scenes and scene swing surveys are the ideas for reminding local and wooded area park executives and scene placement. The maximum precise analysis of panorama visualization, forest park landscapes, planning of essential scenic spots, and visualization and safety of Geographic Information Systems (GIS) cost tools is primarily based on management and historical data. Visual impacts and cultural, historical past adjustments to are expecting visual and natural landscapes. By deciding on points from a panorama attitude, intervention calls for vital signs and symptoms of safety. Among them, studies are being carried out in many towns around the arena. Geographic Information System (GIS) depends simply on noticeable assessment and is remembered for the social legacy plan. The Gate Array (FPGA) and Geographic Information System (GIS) are analyzed based on the panorama in every place within the painting's framework. In these activities, landscape planning is proposed thru the development of scientific Piedmont. Turin is conducting a few research. From a global attitude, the version suggests that these packages reduce the capability use of the era.     

Extraction of melody from polyphonic music using modified morlet wavelet

In this paper, the extraction of melody for polyphonic music is discussed. The method proposes a simple but effective modification in the Morlet wavelet as a modified Morlet wavelet (MMW). The pitch detection concept is applied to the transformed wavelet music signal for primary melody estimation and extraction. The proposed method is then compared with three other methods reported in the literature (the method focused on Short Time Fourier Transform, multi-resolution Fast Fourier Transform, and Morlet Wavelet). The percentage gross pitch error (GPE) parameter is considered for purposes of comparison. In comparison, it is observed that the proposed approach has the lowest GPE. The simulation results also present a lower percentage error in the predominant pitch frequency estimation for the proposed method.     

Simulation of dance performance analysis with microprocessor and machine vision interaction

Dance is the expression of artists' favourite art forms such as express emotions, their body language, and the combination of dance art and stage effects in dance performance. In almost all genres of the art form, the effectiveness of the dance performance is largely due to the quality of the individual dancer's and group dance performance. Performing arts, particularly dance, it is one of the most important of intangible cultural heritage. However, due to the preservation, documentation, analysis, and visualization understanding of dance mode, it is difficult because of technical difficulties relations. The Proposed Machine Learning Support Decision Vector Machine (SDVM) algorithm and Field Programmable Gate Array (FPGA) is a dance expert watching dance due to the recognition task, the task knowledge of professional forecasters, gestures, and facial expressions and face-to-face conditions led to better synchronization of timing. In the proposed Machine learning SDVM algorithm, the results show that positive and dancers in the audience increased negative emotions; acceleration rate and body movement also increased. SDVM is classified as dancer performance based on the artist's facial expressions, stage performance, emotions. The simulation results show good results compared to other methods.     

Efficient FPGA implementation and verification of difference expansion based reversible watermarking with improved time and resource utilization

This paper presents Xilinx System Generator (XSG) model design for realization of reversible watermarking algorithm using Difference Expansion (DE) approach in System-On-Chip (SoC) Field Programmable Gate Array (FPGA) environment. The reversible watermarking is verified by taking a (4 × 4) sized test image and is applicable for larger sizes of cover images. The outcomes of the result demonstrate that the proposed structural design allows combining MATLAB-Simulink and XSG during graphical user interface for image processing applications. The superiority of the algorithm is justified by using comparative analysis with some well-known methods in both software and hardware environments. The method provides effectively higher PSNR at higher embedding capacity. It is also found that the method requires less time and hardware resources with throughput of 13.516 Mb/s at operational frequency of 80 MHz for real time implementation using FPGA.     

Network analysis and characterization of vulnerability in flood control infrastructure for system-level risk reduction

The number of catastrophic events such as extreme rainfalls and hurricanes has been growing. These events pose a major threat to the life safety and economic prosperity of urban regions. Flood control networks play a pivotal role in mitigating the risk associated with the stormwater generated by extreme rainfalls and hurricanes. The objective of this study is to propose a framework to examine the vulnerability in flood control infrastructure networks. This framework applies graph theory concepts and tools to define a vulnerability index for flood control network components (e.g., channels and rivers). The topological attributes of flood control networks are used to determine the vulnerability index based on structural attributes of flood control networks. First, a flood control network is modeled as a directed graph and storage facilities are incorporated into the network. Second, co-location exposure, upstream channel susceptibility, and discharge redundancy are characterized as important vulnerability attributes of a channel in flood control network. Then, these three characteristics are formulized based on the topological attributes of the network and characteristics of channels. The vulnerability index is then determined based on the three vulnerability characteristics. The proposed vulnerability index can be used to evaluate the impact of different risk reduction policies on flood control network vulnerability and determine the optimal mitigation strategies aiming at flood risk reduction, such as widening vulnerable channels, placement of storage facilities in the network or increasing the redundancy of the network. The framework is implemented on two watersheds in Harris County (Texas, USA) and the results' implications for decision-making in infrastructure management and hazard mitigation planning are discussed. The results highlight the capability of the proposed graph-based framework to inform flood risk reduction through evaluation of the vulnerability of infrastructure networks.     

Making costly manufacturing smart with transfer learning under limited data: A case study on composites autoclave processing

The integration of advanced manufacturing processes with ground-breaking Artificial Intelligence methods continue to provide unprecedented opportunities towards modern cyber-physical manufacturing processes, known as smart manufacturing or Industry 4.0. However, the “smartness” level of such approaches closely depends on the degree to which the implemented predictive models can handle uncertainties and production data shifts in the factory over time. In the case of change in a manufacturing process configuration with no sufficient new data, conventional Machine Learning (ML) models often tend to perform poorly. In this article, a transfer learning (TL) framework is proposed to tackle the aforementioned issue in modeling smart manufacturing. Namely, the proposed TL framework is able to adapt to probable shifts in the production process design and deliver accurate predictions without the need to re-train the model. Armed with sequential unfreezing and early stopping methods, the model demonstrated the ability to avoid catastrophic forgetting in the presence of severely limited data. Through the exemplified industry-focused case study on autoclave composite processing, the model yielded a drastic (88%) improvement in the generalization accuracy compared to the conventional learning, while reducing the computational and temporal cost by 56%.     

Decision-based process planning for wire and arc additively manufactured and machined parts

The conventional manufacturing of aircraft components is based on the machining from bulk material and the buy-to-fly ratio is high. This, in combination with the often low machinability of the materials in use, leads to high manufacturing costs. To reduce the production costs for these components, a process chain was developed, which consists of an additive manufacturing process and a machining process. To fully utilize the process chain’s capabilities, an integrated process planning approach is necessary. As a result, the work sequence can be optimized to achieve the economically most suitable sequence. In this paper, a method for a joint manufacturing cost calculation and subsequent decision-based cost minimization is proposed for the wire and arc additive manufacturing (WAAM) & milling process chain. Furthermore, the parameters’ influence on the results and the magnitude of their influence are determined. These results make it possible to design an economically optimal work sequence and to automate the process planning for this process chain.