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A mMSA-FOFPID controller for AGC of multi-area power system with multi-type generations 用于多类型发电的多区域电力系统 AGC 的 mMSA-FOFPID 控制器
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-10-26 DOI: 10.1016/j.suscom.2024.101046
Dillip Khamari , Rabindra Kumar Sahu , Sidhartha Panda , Yogendra Arya
The exceptional growth in the penetration of renewable sources as well as complex and variable operating conditions of load demand in power system may jeopardize its operation without an appropriate automatic generation control (AGC) methodology. Hence, an intelligent resilient fractional order fuzzy PID (FOFPID) controlled AGC system is presented in this study. The parameters of controller are tuned utilizing a modified moth swarm algorithm (mMSA) inspired by the movement of moth towards moon light. At first, the effectiveness of the controller is verified on a nonlinear 5-area thermal power system. The simulation outcomes bring out that the suggested controller provides the best performance over the lately published strategies. In the subsequent step, the methodology is extended to a 5-area system having 10-units of power generations, namely thermal, hydro, wind, diesel, gas turbine with 2-units in each area. It is observed that mMSA based FOFPID is more effective related to other approaches. In order to establish the robustness of the controller, a sensitivity examination is executed. Then, experiments are conducted on OPAL-RT based real-time simulation to confirm the feasibility of the method. Finally, mMSA based FOFPID controller is observed superior than the recently published approaches for standard 2-area thermal and IEEE 10 generator 39 bus systems.
可再生能源渗透率的超常增长以及电力系统复杂多变的负载需求运行条件,可能会在没有适当的自动发电控制(AGC)方法的情况下危及电力系统的运行。因此,本研究提出了一种智能弹性分数阶模糊 PID(FOFPID)控制 AGC 系统。控制器参数的调整采用了一种改进的飞蛾群算法(mMSA),其灵感来自飞蛾对月光的移动。首先,在一个非线性 5 区域火力发电系统上验证了控制器的有效性。仿真结果表明,与最近发布的策略相比,建议的控制器性能最佳。随后,该方法被扩展到一个 5 区域系统,该系统有 10 个发电单元,即火力发电、水力发电、风力发电、柴油发电和燃气轮机发电,每个区域有 2 个发电单元。结果表明,基于 mMSA 的 FOFPID 比其他方法更有效。为了确定控制器的鲁棒性,进行了灵敏度检查。然后,在基于 OPAL-RT 的实时仿真中进行了实验,以确认该方法的可行性。最后,在标准 2 区域热系统和 IEEE 10 发电机 39 总线系统中,基于 mMSA 的 FOFPID 控制器优于最近发布的方法。
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引用次数: 0
Energy-efficient trajectory optimization algorithm based on K-medoids clustering and gradient-based optimizer for multi-UAV-assisted mobile edge computing systems 基于 K-medoids 聚类和梯度优化器的多无人机辅助移动边缘计算系统节能轨迹优化算法
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-10-24 DOI: 10.1016/j.suscom.2024.101045
Mohamed Abdel-Basset , Reda Mohamed , Doaa El-Shahat , Karam M. Sallam , Ibrahim M. Hezam , Nabil M. AbdelAziz
The mobile edge computing system supported by multiple unmanned aerial vehicles (UAVs) has gained significant interest over the last few decades due to its flexibility and ability to enhance communication coverage. In this system, the UAVs function as edge servers to offer computing services to Internet of Things devices (IoTDs), and if they are located distant from those devices, a significant amount of energy is consumed while data is transmitted. Therefore, optimizing UAVs’ trajectories is an indispensable process to minimize overall energy consumption in this system. This problem is difficult to solve because it requires multiple considerations, including the number and placement of stop points (SPs), their order, and the association between SPs and UAVs. A few studies in the literature have been presented to address all of these aspects; nevertheless, the majority of them suffer from slow convergence speed, stagnation in local optima, and expensive computational costs. Therefore, this study presents a new trajectory optimization algorithm, namely ITPA-GBOKM, based on a newly proposed transfer-based encoding mechanism, gradient-based optimizer, and K-Medoids Clustering algorithm to tackle this problem more accurately. The K-medoid clustering algorithm is used to achieve better association between UAVs and SPs since it is less sensitive to outliers than the K-means clustering algorithm; the transfer function-based encoding mechanism is used to efficiently define this problem’s solutions and manage the number of SPs; and GBO is utilized to search for the best SPs that could minimize overall energy consumption, including that consumed by UAVs and IoTDs. The proposed ITPA-GBOKM is evaluated using 13 instances with several IoTDs ranging from 60 to 700 to show its effectiveness in dealing with the trajectory optimization problem at small, medium, and large scales. Furthermore, it is compared to several rival optimizers using a variety of performance metrics, including average fitness, multiple comparison test, Wilcoxon rank sum test, standard deviation, Friedman mean rank, and convergence speed, to show its superiority. The experimental results indicates that this algorithm is capable of producing significantly different and superior results compared to the rival algorithms.
过去几十年来,由多架无人飞行器(UAV)支持的移动边缘计算系统因其灵活性和增强通信覆盖范围的能力而备受关注。在该系统中,无人飞行器充当边缘服务器,为物联网设备(IoTD)提供计算服务,如果无人飞行器距离这些设备较远,则在传输数据时会消耗大量能源。因此,优化无人机的飞行轨迹是最大限度降低该系统整体能耗的一个不可或缺的过程。这个问题很难解决,因为它需要多方面的考虑,包括停止点(SP)的数量和位置、它们的顺序,以及停止点和无人飞行器之间的关联。文献中的一些研究解决了所有这些方面的问题;然而,它们中的大多数都存在收敛速度慢、停滞在局部最优点以及计算成本高昂等问题。因此,本研究基于新提出的基于转移的编码机制、基于梯度的优化器和 K-Medoids 聚类算法,提出了一种新的轨迹优化算法,即 ITPA-GBOKM,以更精确地解决这一问题。与 K-means 聚类算法相比,K-medoid 聚类算法对异常值的敏感度较低,因此能更好地实现无人机和 SP 之间的关联;基于转移函数的编码机制可用于有效定义该问题的解决方案和管理 SP 的数量;梯度优化器可用于搜索最佳 SP,从而最大限度地降低总体能耗,包括无人机和 IoTD 的能耗。建议的 ITPA-GBOKM 使用 13 个 IoTD 实例进行了评估,IoTD 从 60 个到 700 个不等,以显示其在处理小、中、大规模轨迹优化问题时的有效性。此外,还使用多种性能指标(包括平均拟合度、多重比较检验、Wilcoxon 秩和检验、标准偏差、Friedman 平均秩和收敛速度)将其与几种竞争对手的优化器进行了比较,以显示其优越性。实验结果表明,与竞争对手的算法相比,该算法能够产生明显不同的优越结果。
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引用次数: 0
Energy-efficient and fault-tolerant routing mechanism for WSN using optimizer based deep learning model 基于深度学习模型的 WSN 节能容错路由机制
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-10-20 DOI: 10.1016/j.suscom.2024.101044
B. Swathi , Dr. M. Amanullah , S.A. Kalaiselvan
Fault tolerance is the network's capacity to continue operating normally in the event of sensor failure. Sensor nodes in wireless sensor networks (WSNs) may fail due to various reasons, such as energy depletion or environmental damage. Sensor battery drain is the leading cause of failure in WSNs, making energy-saving crucial to extending sensor lifespan. Fault-tolerant protocols use fault recovery methods to ensure network reliability and resilience. Many issues can affect a network, such as communication module breakdown, battery drain, or changes in network architecture. Our proposed FT-RR protocol is a WSN routing protocol that is both reliable and fault-tolerant; it attempts to prevent errors by anticipating them. FT-RR uses Bernoulli's rule to find trustworthy nodes and then uses those pathways to route data to the base station as efficiently as possible. When CHs have greater energy, they construct these pathways. Based on the simulation findings, our approach outperforms the other protocols concerning the rate of loss of packet, end-to-end latency, and network lifespan.
容错性是指网络在传感器发生故障时继续正常运行的能力。无线传感器网络(WSN)中的传感器节点可能会因能量耗尽或环境破坏等各种原因而失效。传感器电池耗尽是 WSN 出现故障的主要原因,因此节能对延长传感器寿命至关重要。容错协议使用故障恢复方法来确保网络的可靠性和弹性。许多问题都会影响网络,如通信模块故障、电池耗尽或网络架构变化。我们提出的 FT-RR 协议是一种既可靠又容错的 WSN 路由协议;它试图通过预测错误来防止错误的发生。FT-RR 使用伯努利法则寻找可信节点,然后利用这些路径尽可能高效地将数据路由到基站。当 CH 拥有更多能量时,它们就会构建这些路径。根据模拟结果,我们的方法在数据包丢失率、端到端延迟和网络寿命方面优于其他协议。
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引用次数: 0
Fuzzy based Energy Efficient Rider Remora Routing protocol for secured communication in WSN network 用于 WSN 网络安全通信的基于模糊技术的高能效 Rider Remora 路由协议
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-10-18 DOI: 10.1016/j.suscom.2024.101043
R.M. Bhavadharini , Suseela Sellamuthu , G. Sudhakaran , Ahmed A. Elngar
Due to the resource constraint nature of WSN, ensuring secured communication in WSN is a challenging problem. Moreover, enhancing the network lifetime is one of the major issues faced by the existing studies. So, in order to secure the communication between WSNs and achieve improved network lifetime, a novel trust enabled routing protocol is proposed in this study. Initially, the clusters are constructed using Direct, Indirect, and Total Trust evaluations, which helps to identify the faulty nodes. After, an Improved Fuzzy-based Balanced Cost Cluster Head Selection (IFBECS) method is used to choose the cluster head (CH). Finally, to determine the best path from source to destination, a hybrid bionic energy-efficient routing model known as an Energy Efficient Rider Remora Routing (EERRR) protocol is introduced. To improve the network lifetime and throughput, the parameters like remaining energy of the CH, sensor node space, CH, etc., are considered by the utilized protocol. The proposed mechanism is implemented in NS-2 programming tool. The simulation results show that the proposed routing protocol has attained improved PDR of 97.92 % at the time period of 50 ms, reduced energy consumption of 3.336 at the time period of 100 ms, higher throughput of 86262.7 at the time period of 250 ms, and enhanced network lifetime of 1028.08 rounds in 200 nodes. Therefore, by attaining better results as compared with other existing protocols, it is clearly revealed that the proposed routing protocol is highly suitable for secured energy efficient WSN communication.
由于 WSN 的资源限制特性,确保 WSN 通信安全是一个具有挑战性的问题。此外,提高网络寿命也是现有研究面临的主要问题之一。因此,为了确保 WSN 之间的通信安全并提高网络寿命,本研究提出了一种新型的信任路由协议。首先,使用直接、间接和总信任评估构建簇,这有助于识别故障节点。然后,使用基于改进模糊平衡成本的簇头选择(IFBECS)方法来选择簇头(CH)。最后,为了确定从源到目的地的最佳路径,引入了一种混合仿生节能路由模型,即节能骑乘者路由(ERRR)协议。为了提高网络寿命和吞吐量,该协议考虑了 CH 的剩余能量、传感器节点空间、CH 等参数。所提出的机制是在 NS-2 编程工具中实现的。仿真结果表明,所提出的路由协议在 50 ms 的时间周期内提高了 PDR 97.92 %,在 100 ms 的时间周期内降低了能耗 3.336,在 250 ms 的时间周期内提高了吞吐量 86262.7,在 200 个节点中提高了网络寿命 1028.08 轮。因此,与其他现有协议相比,所提出的路由协议取得了更好的结果,清楚地表明它非常适用于安全节能的 WSN 通信。
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引用次数: 0
A certain examination on heterogeneous systolic array (HSA) design for deep learning accelerations with low power computations 针对低功耗计算深度学习加速的异构系统阵列(HSA)设计的若干研究
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-10-11 DOI: 10.1016/j.suscom.2024.101042
Dinesh Kumar Jayaraman Rajanediran , C. Ganesh Babu , K. Priyadharsini
Acceleration techniques play a crucial role in enhancing the performance of modern high-speed computations, especially in Deep Learning (DL) applications where the speed is of utmost importance. One essential component in this context is the Systolic Array (SA), which effectively handles computational tasks and data processing in a rhythmic manner. Google's Tensor Processing Unit (TPU) leverages the power of SA for neural networks. The core SA's functionality and performance lies in the Computation Element (CE), which facilitates parallel data flow. In our article, we introduce a novel approach called Proposed Systolic Array (PSA), which is implemented on the CE and further enhanced with a modified Hybrid Kogge Stone adder (MHA). This design incorporates principles to expedite computations by rounding and extracting data model in SA as PSA-MHA. The PSA, utilizing a data flow model with MHA, significantly accelerates data shifts and control passes in execution cycles. We validated our approach through simulations on the Cadence Virtuoso platform using 65 nm process technology, comparing it to the General Matrix Multiplication (GMMN) benchmark. The results showed remarkable improvements in the CE, with a 30.29 % reduction in delay, a 23.07 % reduction in area, and an 11.87 % reduction in power consumption. The PSA outperformed these improvements, achieving a 46.38 % reduction in delay, a 7.58 % reduction in area, and an impressive 48.23 % decrease in Area Delay Product (ADP). To further substantiate our findings, we applied the PSA-based approach to pre-trained hybrid Convolutional and Recurrent (CNN-RNN) neural models. The PSA-based hybrid model incorporates 189 million Multiply-Accumulate (MAC) units, resulting in a weighted mean architecture value of 784.80 for the RNN component. We also explored variations in bit width, which led to delay reductions ranging from 20.17 % to 30.29 %, area variations between 13.08 % and 32.16 %, and power consumption changes spanning from 11.88 % to 20.42 %.
加速技术在提高现代高速计算性能方面发挥着至关重要的作用,尤其是在速度至关重要的深度学习(DL)应用中。在这种情况下,系统阵列(SA)就是一个重要的组成部分,它能以有节奏的方式有效处理计算任务和数据处理。谷歌的张量处理单元(TPU)就利用了SA在神经网络中的强大功能。SA的核心功能和性能在于计算元件(CE),它能促进并行数据流。在我们的文章中,我们介绍了一种名为 "拟议收缩阵列"(PSA)的新方法,它是在 CE 上实现的,并通过改进的混合 Kogge Stone 加法器(MHA)得到了进一步增强。这种设计包含了通过舍入和提取 SA 中的数据模型来加快计算速度的原则,即 PSA-MHA。PSA 利用 MHA 的数据流模型,大大加快了执行周期中的数据转移和控制传递。我们在采用 65 纳米工艺技术的 Cadence Virtuoso 平台上进行了仿真,并将其与通用矩阵乘法 (GMMN) 基准进行了比较,从而验证了我们的方法。结果表明,CE 有了明显改善,延迟减少了 30.29%,面积减少了 23.07%,功耗减少了 11.87%。PSA 的改进幅度超过了这些改进,延迟减少了 46.38%,面积减少了 7.58%,面积延迟积(ADP)减少了 48.23%,令人印象深刻。为了进一步证实我们的研究结果,我们将基于 PSA 的方法应用于预先训练好的混合卷积和递归(CNN-RNN)神经模型。基于 PSA 的混合模型包含 1.89 亿个乘积 (MAC) 单元,因此 RNN 部分的加权平均架构值为 784.80。我们还探索了位宽的变化,结果是延迟降低了 20.17% 到 30.29%,面积变化了 13.08% 到 32.16%,功耗变化了 11.88% 到 20.42%。
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引用次数: 0
A bidirectional gated recurrent unit based novel stacking ensemble regressor for foretelling the global horizontal irradiance 用于预测全球水平辐照度的基于双向门控递归单元的新型叠加集合回归器
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-10-09 DOI: 10.1016/j.suscom.2024.101041
Rahul Gupta , Aseem Chandel
The rapid expansion of solar power generation has led to new challenges in solar intermittency, requiring precise forecasts of Global Horizontal Irradiance (GHI). Accurate GHI predictions are crucial for integrating sustainable energy sources into traditional electrical grid management. The article proposes an innovative solution, the novel Enhanced Stack Ensemble with a Bi-directional Gated Recurrent Unit (ESE-Bi-GRU), which uses machine learning (ML) boosting regressors such as Ada Boost, Cat Boost, Extreme Gradient Boost, and Gradient Boost, and Light Gradient Boost Machine acts as a base learner and the deep learning (DL) algorithms such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for both directions are taken as a meta-learner. The predictive performance of the proposed ESE-Bi-GRU model is evaluated against individual models, showing significant reductions in mean absolute error (MAE) by 86.03 % and root mean squared error (RMSE) by 66.43 %. The model's ability to minimize prediction errors, such as MAE and RMSE holds promise for more effective planning and utilization of sporadic solar resources. By improving GHI forecast accuracy, the ESE-Bi-GRU model contributes to optimizing the integration of sustainable energy sources within the broader energy grid, fostering a more sustainable and environmentally conscious approach to energy management.
太阳能发电的快速发展带来了太阳能间歇性的新挑战,需要对全球水平辐照度(GHI)进行精确预测。准确预测全球水平辐照度对于将可持续能源纳入传统电网管理至关重要。文章提出了一种创新的解决方案--具有双向门控递归单元的新型增强堆集合(ESE-Bi-GRU),它使用机器学习(ML)提升回归器,如 Ada Boost、Cat Boost、Extreme Gradient Boost 和 Light Gradient Boost Machine,并将 Light Gradient Boost Machine 作为基础学习器,将双向的深度学习(DL)算法,如长短期记忆(LSTM)和门控递归单元(GRU)作为元学习器。与单个模型相比,对所提出的 ESE-Bi-GRU 模型的预测性能进行了评估,结果显示平均绝对误差 (MAE) 显著降低了 86.03%,均方根误差 (RMSE) 显著降低了 66.43%。该模型能够最大限度地减少 MAE 和 RMSE 等预测误差,为更有效地规划和利用零星太阳能资源带来了希望。通过提高 GHI 预测精度,ESE-Bi-GRU 模型有助于在更广泛的能源网中优化可持续能源的整合,促进更具可持续性和环保意识的能源管理方法。
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引用次数: 0
Occupancy prediction: A comparative study of static and MOTIF time series features using WiFi Syslog data 占用预测:利用 WiFi 系统日志数据对静态和 MOTIF 时间序列特征进行比较研究
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-10-04 DOI: 10.1016/j.suscom.2024.101040
Bassam A. Abdelghani, Ahlam Al Mohammad, Jamal Dari, Mina Maleki, Shadi Banitaan
Occupancy prediction has been the subject of ongoing research, employing various methods and data sources to improve occupancy prediction accuracy and energy efficiency in buildings. Precise occupancy prediction is crucial for optimizing energy usage, ensuring occupant comfort, and enhancing building management. With the increasing demand for intelligent building management systems, robust and accurate occupancy prediction models are becoming more critical. This study aims to predict building occupancy using WiFi Syslog files from three different datasets: an open-source dataset from the University of Massachusetts Dartmouth, a new locally collected dataset from the dental school at the University of Detroit Mercy, and finally, a dataset from an office building in Berkeley, California. Two types of features, static features, and MOTIF time series features, were extracted from the datasets to process and compare their performance in occupancy prediction.
The first step of the proposed framework consisted of selecting the most suitable time range to compare occupancy prediction models between different datasets. It was concluded that this analysis was best conducted semester by semester. Multiple regression algorithms, such as random forest and LightGBM, were applied in the following step, along with advanced ensemble techniques, including stacking and blending, to assess the model. The stacking regression showed the best results for static features across all datasets. It achieved a Coefficient of Determination (R2) of 0.9540 in the first dataset, 0.9482 in the second, and 0.9977 in the third. For MOTIF features, however, the best algorithm depended on the dataset. All algorithms performed similarly in the first dataset, with R2 of 0.956. In contrast, LightGBM and the Stacking Regressor had better results than the others in the second dataset, with a low R2 of 0.531 due to dataset-specific differences. The stacking regression once again delivered the best results in the last dataset with an R2 of 0.9967.
占用率预测一直是持续研究的主题,它采用各种方法和数据源来提高占用率预测的准确性和建筑物的能源效率。精确的占用预测对于优化能源使用、确保居住舒适度和加强楼宇管理至关重要。随着对智能楼宇管理系统的需求日益增长,稳健而准确的占用预测模型变得越来越重要。本研究旨在利用来自三个不同数据集的 WiFi 系统日志文件预测楼宇占用情况:一个来自马萨诸塞大学达特茅斯分校的开源数据集,一个来自底特律梅西大学牙科学院的本地收集的新数据集,以及一个来自加利福尼亚州伯克利办公楼的数据集。从数据集中提取了两种类型的特征,即静态特征和 MOTIF 时间序列特征,以处理和比较它们在占用率预测中的性能。得出的结论是,这种分析最好按学期进行。在接下来的步骤中,应用了随机森林和 LightGBM 等多重回归算法,以及包括堆叠和混合在内的高级集合技术来评估模型。在所有数据集的静态特征方面,堆叠回归显示出最佳结果。第一个数据集的判定系数(R2)为 0.9540,第二个数据集为 0.9482,第三个数据集为 0.9977。然而,对于 MOTIF 特征,最佳算法取决于数据集。在第一个数据集中,所有算法的表现相似,R2 为 0.956。相比之下,LightGBM 和堆叠回归算法在第二个数据集中的表现要好于其他算法,但由于特定数据集的差异,R2 较低,仅为 0.531。在最后一个数据集中,堆叠回归再次取得了最佳结果,R2 为 0.9967。
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引用次数: 0
A scenario-customizable and visual-rendering simulator for on-vehicle vibration energy harvesting 用于车载振动能量采集的可定制场景和可视化渲染模拟器
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-09-23 DOI: 10.1016/j.suscom.2024.101039
Fangcheng Guo , Jingjin Li , Chung Ket Thein , Anqi Gao , Jianfeng Ren , Chang Heon Lee , Jiawei Li , Tianxiang Cui , Heng Yu
The rising demand for renewable energy supply in standalone computing devices has led to the emergence of vibration energy harvesting (VEH) to overcome technical and environmental challenges. For instance, VEH is desirable in IoT scenarios where maintaining a battery supply is non-sustainable or impractical due to many devices or remote circumstances. VEH can be environmentally friendly given that it reduces the reliance on traditional battery production and usage, thus reducing the carbon footprint and chemical waste in disposable batteries. However, a significant hurdle in VEH adoption is the lack of effective simulation tools for generating various application scenarios to describe, validate, or predict the efficacy of the VEH-based devices. It is necessary for designing and implementing a VEH simulator for a variety of realistic application scenarios. Being the first of its kind, this study presents a scenario-customizable and visual-rendering VEH simulation system based on the Unity3D Engine. The proposed simulator features a modular design that consists of several key functional components including vibration scenarios’ creation and manipulation, VEH model specification, Unity-Python Co-computing mechanism, and 3D visualization. This paper also presents two AI-based case studies leveraging the functionality and data provided by the simulator to demonstrate its potential for data-driven research and applications.
独立计算设备对可再生能源供应的需求日益增长,因此出现了振动能量采集(VEH)技术,以克服技术和环境挑战。例如,在物联网应用场景中,由于设备众多或环境偏远,维持电池供应是不可持续或不切实际的,这时就需要振动能量收集。VEH 可以减少对传统电池生产和使用的依赖,从而减少一次性电池的碳足迹和化学废物,因此非常环保。然而,采用 VEH 的一个重大障碍是缺乏有效的模拟工具来生成各种应用场景,以描述、验证或预测基于 VEH 的设备的功效。因此,有必要为各种现实应用场景设计和实施 VEH 模拟器。本研究首次提出了基于 Unity3D 引擎的场景定制和视觉渲染 VEH 模拟系统。该模拟器采用模块化设计,由多个关键功能组件组成,包括振动场景创建和操作、VEH 模型规范、Unity-Python 协同计算机制和三维可视化。本文还介绍了两个基于人工智能的案例研究,利用模拟器提供的功能和数据,展示其在数据驱动的研究和应用方面的潜力。
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引用次数: 0
Incorporation of computational routines in a microservice architecture in AgDataBox platform 在 AgDataBox 平台的微服务架构中纳入计算例程
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-09-21 DOI: 10.1016/j.suscom.2024.101038
Ricardo Sobjak , Eduardo Godoy de Souza , Claudio Leones Bazzi , Kelyn Schenatto , Nelson Miguel Betzek , Alan Gavioli
Agriculture has been undergoing a digital process that aims to apply digital technologies to make the sector more productive, profitable, and environmentally responsible. This trend has been adopted since applying precision agriculture (PA) techniques and, more recently, with digital agriculture (DA). DA aims to use all available information and knowledge to enable the automation of sustainable processes in agriculture, applying data analysis methods and techniques by specific software and platforms to collect and transform data into meaningful information for agriculture. Platform AgDataBox (ADB) offers tools to allow agriculture specialists to obtain, process, and visualize data for the correct decision-making. However, its structure needed to be readjusted to new software architecture to allow the aggregation of new functionalities and expand the ADB platform. This study aimed to develop a web microservices architecture (ADB-MSA) to incorporate the required functionalities to create thematic maps (TMs) and delineate management zones (MZs). ADB-MSA provided eight microservices, six of which (statistics, spatial, interpolation, clustering, rectification, and lime/nutrient recommendation) execute procedures based on JavaScript, R, and Python programming languages. At the same time, the other two are used to store data. In the case study, the procedures to create TMs and delineate MZs were performed with data from one commercial area. Thus, the services provided in the architecture meet the steps of creating TMs and delineating MZs, as MZs for fertilizer application were generated and evaluated according to phosphorus and potassium requirements. ADB-MSA allows the development of several new client applications (web, mobile, desktop, and embedded systems) to promote solutions in agriculture, streamlining processes, as it abstracts the implementation and execution complexity of available algorithms.
农业一直在经历数字化进程,目的是应用数字技术提高农业的生产率、利润和环境责任。自从应用精准农业(PA)技术,以及最近的数字农业(DA)技术以来,这一趋势已被采纳。数字农业旨在利用所有可用信息和知识,实现农业可持续流程的自动化,通过特定软件和平台应用数据分析方法和技术,收集数据并将其转化为对农业有意义的信息。AgDataBox 平台(ADB)提供了各种工具,使农业专家能够获取、处理和可视化数据,从而做出正确的决策。然而,其结构需要根据新的软件架构进行重新调整,以便聚合新的功能并扩展 ADB 平台。本研究旨在开发一个网络微服务架构(ADB-MSA),以整合创建专题地图(TM)和划定管理区(MZ)所需的功能。ADB-MSA 提供了八个微服务,其中六个(统计、空间、插值、聚类、校正和石灰/养分推荐)执行基于 JavaScript、R 和 Python 编程语言的程序。同时,另外两个用于存储数据。在案例研究中,创建 TM 和划分 MZ 的程序是利用一个商业区的数据执行的。因此,架构中提供的服务满足了创建临时管理区和划定管理区的步骤,因为根据磷和钾的需求生成并评估了施肥的管理区。ADB-MSA 允许开发多个新的客户端应用程序(网络、移动、桌面和嵌入式系统),以推广农业解决方案,简化流程,因为它抽象了现有算法的实施和执行复杂性。
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引用次数: 0
Optimization of reservoir operation by sine cosine algorithm: A case of study in Algeria 利用正弦余弦算法优化水库运行:阿尔及利亚研究案例
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2024-09-11 DOI: 10.1016/j.suscom.2024.101035
Merouane Boudjerda , Bénina Touaibia , Mustapha Kamel Mihoubi , Ozgur Kisi , Mohammd Ehteram , Ahmed El-Shafie
The optimal operation of the reservoir has vital importance in water engineering. In the presented article, a new optimization method, named sine cosine algorithm (SCA) was employed to obtain operating policy for an irrigation system. The SCA was utilized for the monthly operation of the Boukerdane Dam placed in the north of Algeria. The fitness function was the minimization of the total shortage for the studied period. Three scenarios considering three different seasons of inflow (dry, normal and wet) are used to optimize the reservoir system’s operation. The SCA outputs were compared with particle swarm optimization (PSO) and kidney algorithm (KA). The outcomes indicated that the SCA surpassed the PSO and KA in convergence rate. The general results indicated the low speed of KA and PSO in achieving convergence. The results indicated that the highest RES (resiliency index), SUS (sustainability index) and REL (reliability index) achieved by the SCA were 65, 86 and 92 %, respectively. Comparing the third scenario with the first and second scenarios, it was observed that the third scenario (wet seasons) improved the results.
水库的优化运行在水利工程中至关重要。本文采用了一种名为正弦余弦算法(SCA)的新优化方法,以获得灌溉系统的运行策略。SCA 被用于阿尔及利亚北部 Boukerdane 大坝的月度运行。拟合函数是研究期间总短缺量的最小化。考虑到三个不同的流入季节(干旱、正常和潮湿),采用了三种方案来优化水库系统的运行。将 SCA 输出与粒子群优化(PSO)和肾算法(KA)进行了比较。结果表明,SCA 的收敛速度超过了 PSO 和 KA。总体结果表明,KA 和 PSO 的收敛速度较低。结果表明,SCA 达到的最高 RES(弹性指数)、SUS(可持续性指数)和 REL(可靠性指数)分别为 65%、86% 和 92%。将第三种方案与第一种和第二种方案进行比较后发现,第三种方案(雨季)的结果有所改善。
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Sustainable Computing-Informatics & Systems
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