Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

To protect the environment,the discharged sewage’s quality must meet the state’s discharge standards.There are many water quality indicators,and the pH(Potential of Hydrogen)value is one of them.The natural water’s pH value is 6.0–8.5.The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard.This paper aims to study the deep learning prediction model of wastewater’s pH.Firstly,the research uses the random forest method to select the data features and then,based on the sliding window,convert the data set into a time series which is the input of the deep learning training model.Secondly,by analyzing and comparing relevant references,this paper believes that the CNN(Convolutional Neural Network)model is better at nonlinear data modeling and constructs a CNN model including the convolution and pooling layers.After alternating the combination of the convolutional layer and pooling layer,all features are integrated into a full-connected neural network.Thirdly,the number of input samples of the CNN model directly affects the prediction effect of the model.Therefore,this paper adopts the sliding window method to study the optimal size.Many experimental results show that the optimal prediction model can be obtained when alternating six convolutional layers and three pooling layers.The last full-connection layer contains two layers and 64 neurons per layer.The sliding window size selects as 12.Finally,the research has carried out data prediction based on the optimal CNN deep learning model.The predicted pH of the sewage is between 7.2 and 8.6 in this paper.The result is applied in the monitoring system platform of the“Intelligent operation and maintenance platform of the reclaimed water plant.”

Path planning based on sliding window and variant A* algorithm for quadruped robot

In order to improve the adaptability of the quadruped robot in complex environments,a path planning method based on sliding window and variant A* algorithm for quadruped robot is presented. To improve the path planning efficiency and robot security,an incremental A* search algorithm( IA*) and the A* algorithm having obstacle grids extending( EA*) are proposed respectively. The IA* algorithm firstly searches an optimal path based on A* algorithm,then a new route from the current path to the new goal projection is added to generate a suboptimum route incrementally. In comparison with traditional method solving path planning problem from scratch,the IA* enables the robot to plan path more efficiently. EA* extends the obstacle by means of increasing grid g-value,which makes the route far away from the obstacle and avoids blocking the narrow passage. To navigate the robot running smoothly,a quadratic B-spline interpolation is applied to smooth the path.Simulation results illustrate that the IA* algorithm can increase the re-planning efficiency more than 5 times and demonstrate the effectiveness of the EA* algorithm.

Dynamically Computing Approximate Frequency Counts in Sliding Window over Data Stream

This paper presents two one-pass algorithms for dynamically computing frequency counts in sliding window over a data stream-computing frequency counts exceeding user-specified threshold ε. The first algorithm constructs subwindows and deletes expired sub-windows periodically in sliding window, and each sub-window maintains a summary data structure. The first algorithm outputs at most 1/ε ＋ 1 elements for frequency queries over the most recent N elements. The second algorithm adapts multiple levels method to deal with data stream. Once the sketch of the most recent N elements has been constructed, the second algorithm can provides the answers to the frequency queries over the most recent n （ n≤N） elements. The second algorithm outputs at most 1/ε ＋ 2 elements. The analytical and experimental results show that our algorithms are accurate and effective.

Differentially Private Real-Time Streaming Data Publication Based on Sliding Window Under Exponential Decay

Continuous response of range query on steaming data provides useful information for many practical applications as well as the risk of privacy disclosure.The existing research on differential privacy streaming data publication mostly pay close attention to boosting query accuracy,but pay less attention to query efficiency,and ignore the effect of timeliness on data weight.In this paper,we propose an effective algorithm of differential privacy streaming data publication under exponential decay mode.Firstly,by introducing the Fenwick tree to divide and reorganize data items in the stream,we achieve a constant time complexity for inserting a new item and getting the prefix sum.Meanwhile,we achieve time complicity linear to the number of data item for building a tree.After that,we use the advantage of matrix mechanism to deal with relevant queries and reduce the global sensitivity.In addition,we choose proper diagonal matrix further improve the range query accuracy.Finally,considering about exponential decay,every data item is weighted by the decay factor.By putting the Fenwick tree and matrix optimization together,we present complete algorithm for differentiate private real-time streaming data publication.The experiment is designed to compare the algorithm in this paper with similar algorithms for streaming data release in exponential decay.Experimental results show that the algorithm in this paper effectively improve the query efficiency while ensuring the quality of the query.

Four Sliding Windows Pattern Matching Algorithm (FSW)

This paper presents an efficient pattern matching algorithm (FSW). FSW improves the searching process for a pattern in a text. It scans the text with the help of four sliding windows. The windows are equal to the length of the pattern, allowing multiple alignments in the searching process. The text is divided into two parts;each part is scanned from both sides simultaneously using two sliding windows. The four windows slide in parallel in both parts of the text. The comparisons done between the text and the pattern are done from both of the pattern sides in parallel. The conducted experiments show that FSW achieves the best overall results in the number of attempts and the number of character comparisons compared to the pattern matching algorithms: Two Sliding Windows (TSW), Enhanced Two Sliding Windows algorithm (ETSW) and Berry-Ravindran algorithm (BR). The best time case is calculated and found to be??while the average case time complexity is??.

Influence of Three Sizes of Sliding Windows on Principle Component Analysis Fault Detection of Air Conditioning Systems

Principal component analysis(PCA)has been already employed for fault detection of air conditioning systems.The sliding window,which is composed of some parameters satisfying with thermal load balance,can select the target historical fault-free reference data as the template which is similar to the current snapshot data.The size of sliding window is usually given according to empirical values,while the influence of different sizes of sliding windows on fault detection of an air conditioning system is not further studied.The air conditioning system is a dynamic response process,and the operating parameters change with the change of the load,while the response of the controller is delayed.In a variable air volume(VAV)air conditioning system controlled by the total air volume method,in order to ensure sufficient response time,30 data points are selected first,and then their multiples are selected.Three different sizes of sliding windows with 30,60 and 90 data points are applied to compare the fault detection effect in this paper.The results show that if the size of the sliding window is 60 data points,the average fault-free detection ratio is 80.17%in fault-free testing days,and the average fault detection ratio is 88.47%in faulty testing days.

A graph-based sliding window multi-join over data stream

Join operation is a critical problem when dealing with sliding window over data streams. There have been many optimization strategies for sliding window join in the literature, but a simple heuristic is always used for selecting the join sequence of many sliding windows, which is ineffectively. The graph-based approach is proposed to process the problem. The sliding window join model is introduced primarily. In this model vertex represent join operator and edge indicated the join relationship among sliding windows. Vertex weight and edge weight represent the cost of join and the reciprocity of join operators respectively. Then good query plan with minimal cost can be found in the model. Thus a complete join algorithm combining setting up model, finding optimal query plan and executing query plan is shown. Experiments show that the graph-based approach is feasible and can work better in above environment.

Linked-Tree: An Aggregate Query Algorithm Based on Sliding Window over Data Stream

How to process aggregate queries over data streams efficiently and effectively have been becoming hot re search topics in both academic community and industrial community. Aiming at the issues, a novel Linked-tree algorithm based on sliding window is proposed in this paper. Due to the proposal of concept area, the Linked-tree algorithm reuses many primary results in last window and then avoids lots of unnecessary repeated comparison operations between two successive windows. As a result, execution efficiency of MAX query is improved dramatically. In addition, since the size of memory is relevant to the number of areas but irrelevant to the size of sliding window, memory is economized greatly. The extensive experimental results show that the performance of Linked-tree algorithm has significant improvement gains over the traditional SC （Simple Compared） algorithm and Ranked-tree algorithm.

An Indexed Non-Equijoin Algorithm Based on Sliding Windows over Data Streams

Processing a join over unbounded input streams requires unbounded memory, since every tuple in one infinite stream must be compared with every tuple in the other. In fact, most join queries over unbounded input streams are restricted to finite memory due to sliding window constraints. So far, non-indexed and indexed stream equijoin algorithms based on sliding windows have been proposed in many literatures. However, none of them takes non-equijoin into consideration. In many eases, non-equijoin queries occur frequently. Hence, it is worth to discuss how to process non-equijoin queries effectively and efficiently. In this paper, we propose an indexed join algorithm for supporting non-equijoin queries. The experimental results show that our indexed non-equijoin techniques are more efficient than those without index.

Buffer Management in the Sliding-Window (SW) Packet Switch for Priority Switching

Switch and router architectures employing a shared buffer are known to provide high throughput, low delay, and high memory utilization. Superior performance of a shared-memory switch compared to switches employing other buffer strategies can be achieved by carefully implementing a buffer-management scheme. A buffer-sharing policy should allow all of the output interfaces to have fair and robust access to buffer resources. The sliding-window (SW) packet switch is a novel architecture that uses an array of parallel memory modules that are logically shared by all input and output lines to store and process data packets. The innovative aspects of the SW architecture are the approach to accomplishing parallel operation and the simplicity of the control functions. The implementation of a buffer-management scheme in a SW packet switch is dependent on how the buffer space is organized into output queues. This paper presents an efficient SW buffer-management scheme that regulates the sharing of the buffer space. We compare the proposed scheme with previous work under bursty traffic conditions. Also, we explain how the proposed buffer-management scheme can provide quality-of-service (QoS) to different traffic classes.

Enhanced remote astronomical archive system based on the file-level Unlimited Sliding-Window technique

Data archiving is one of the most critical issues for modern astronomical observations.With the development of a new generation of radio telescopes,the transfer and archiving of massive remote data have become urgent problems to be solved.Herein,we present a practical and robust file-level flow-control approach,called the Unlimited Sliding-Window(USW),by referring to the classic flow-control method in the TCP protocol.Based on the USW and the Next Generation Archive System(NGAS)developed for the Murchison Widefield Array telescope,we further implemented an enhanced archive system(ENGAS)using ZeroMQ middleware.The ENGAS substantially improves the transfer performance and ensures the integrity of transferred files.In the tests,the ENGAS is approximately three to twelve times faster than the NGAS and can fully utilize the bandwidth of network links.Thus,for archiving radio observation data,the ENGAS reduces the communication time,improves the bandwidth utilization,and solves the remote synchronous archiving of data from observatories such as Mingantu spectral radioheliograph.It also provides a better reference for the future construction of the Square Kilometer Array(SKA)Science Regional Center.

基于CNN-LSTM的水泥熟料f-CaO预测模型

水泥熟料中游离氧化钙(f-CaO)含量的传统人工离线检测缺乏时效性,不利于生产指导。针对离线检测的滞后问题和软测量模型中f-CaO含量与辅助变量的时序匹配问题,提出了一种基于卷积神经网络(convolutional neural network,CNN)和长短时记忆(long short-term memory,LSTM)神经网络的f-CaO含量预测模型。首先,利用滑动窗口截取辅助变量的区间数据;然后,采用CNN提取区间数据的时序特征;之后,构建LSTM神经网络模型;最后,控制截取辅助变量的延迟时间和间隔时间,根据模型预测拟合度提取辅助变量的最优时序特征。仿真结果表明,所提模型提高了水泥熟料中f-CaO含量的预测精度。

YOLOv5-LR:一种遥感影像旋转目标检测模型

真实遥感图像中,目标呈现任意方向分布的特点,原始YOLOv5网络存在难以准确表达目标的位置和范围、以及检测速度一般的问题。针对上述问题,提出一种遥感影像旋转目标检测模型YOLOv5-Left-Rotation,首先利用Transformer自注意力机制,让模型更加注意感兴趣的目标,并且在图像预处理过程中采用Mosaic数据增强,对后处理过程使用改进后的非极大值抑制算法Non-Maximum Suppression。其次,引入角度损失函数,增加网络的输出维度,得到旋转矩形的预测框。最后,在网络模型的浅层阶段,增加滑动窗口分支,来提高大尺寸遥感稀疏目标的检测效率。实验数据集为自制飞机数据集CASIA-plane78和公开的舰船数据集HRSC2016,结果表明,改进旋转目标检测算法相比于原始YOLOv5网络的平均精度提升了3.175%,在吉林一号某星推扫出的大尺寸多光谱影像中推理速度提升了13.6%,能够尽可能地减少冗余背景信息,更加准确检测出光学遥感图像中排列密集、分布无规律的感兴趣目标的区域。

基于点线特征融合的实时视惯SLAM算法

为了在光照不足和低纹理场景中实现移动机器人的高精度实时定位和建图,提出了一种基于视觉点线特征以及IMU特征融合的实时SLAM算法。首先通过跳跃路由策略和自适应阈值策略改进了EDlines算法,提高了线特征提取的质量,从而提高了特征跟踪的有效性。然后将视觉惯性特征紧耦合建立约束,通过滑动窗口和边缘化模型进行非线性优化,实现了高精度高实时性的状态估计。实验证明,所提算法在线特征提取的有效性方面优于传统的线段提取算法,同时SLAM系统的定位精度和鲁棒性均得到有效提升。

基于滑窗OPTICS算法和DATA-SSI算法的桥梁模态参数智能化识别

针对现有基于数据驱动的随机子空间(data-driven stochastic subspace identification,DATA-SSI)算法存在的不足,无法实现稳定图中真假模态的智能化筛选,提出了一种新的模态参数智能化识别算法。首先通过引入滑窗技术来实现对输入信号的合理划分,以避免虚假模态和模态遗漏现象的出现;其次通过引入OPTICS(ordering points to identify the clustering structure)密度聚类算法实现稳定图中真实模态的智能化筛选,最后将所提算法运用于某实际大型斜拉桥主梁结构的频率和模态振型识别过程中。结果表明,所提改进算法识别的频率值结果与理论值(MIDAS有限元结果)以及实际值(现场动力特性实测结果)间的误差均在5%以内,且识别的模态振型图与理论模态振型图具有很高的相似性。

基于概念漂移发现的业务过程异常检测方法

现有的业务过程异常检测方法假定业务过程固定不变,忽视因出现概念漂移而导致业务过程模型变化的情况,以致现有的异常检测方法准确率下降。提出一种使用事件日志、基于概念漂移发现的业务过程异常检测方法。构建结合概念漂移发现方法和基于循环神经网络的业务过程异常检测模型,从事件日志中提取事件序列特征数据集,使用该模型中的事件预测模块来预测事件发生的概率,根据事件发生的概率分布来计算事件日志中每个案例的异常分数。异常分数大于设定的异常评分阈值的事件所在的案例被认为是候选异常案例。使用霍夫丁不等式来判断概念漂移是否已发生,并使用双层滑动窗口机制来获取概念漂移案例的发生位置,从候选异常案例提取概念漂移案例。使用增量学习将概念漂移案例用于更新事件预测模块,使过程异常检测模型能够辨别概念漂移案例与真正异常案例,更准确地检测出真正的业务过程异常。实验结果表明,与主流的业务过程异常检测方法相比,所提异常检测方法可以较准确地发现业务过程中的概念漂移,能够更准确地检测出业务过程中发生的异常,对提高业务过程运行的平稳性有积极意义。

个体化频带滑动窗特征的轻度认知障碍诊断研究

轻度认知障碍(MCI)是老年性痴呆诊断的关键阶段,脑电(EEG)信号特征可以反映MCI患者的认知状态,帮助实现早期诊断。现有研究在EEG特征提取过程中,针对脑电各节律,大多采用固定的时间窗完成分段处理,忽略了不同节律的特征差异,从而影响诊断效果。针对该问题,本文提出了一种新的组合滑动窗优化算法,该算法通过迭代振幅调整傅里叶变换(IAAFT)对零模型的构建方法进行了改进,以此得到评估大脑动态特性指标KPLI,通过对EEG各频段信号采取多种滑动窗组合,并以KPLI指标引导,得到适合不同频段的最佳滑动窗组合。在最佳滑动窗组合基础上,对各频段组合提取相位滞后指数(PLI),进行连续小波变换(CWT)特征,通过ResNet-MLP双通道分类网络实现MCI诊断。结果显示,使用个性化组合频段滑动窗对88名受试者(32名MCI患者,36名阿尔茨海默症患者以及20名正常对照组)实现了诊断分类,得到了82.2%的分类准确率,比固定窗的分类提高了10%(得到了72.2%的分类准确率)。结果表明,基于个体化脑电节律特征组合能够更好提取MCI的特征,提高轻度认知障碍诊断的正确率与特异性,是一种有效的脑电特征提取方法。

基于QM-DBSCAN与BiLSTM的风电机组异常工况预警研究

提出一种基于四分位(QM)-具有噪声的基于密度聚类法(DBSCAN)与双向长短期记忆网络(BiLSTM)的风电机组故障预警方法。首先,针对风速-功率图中限功率点难以清洗完全的问题,提出利用QM与DBSCAN联合来对建模运行数据进行预处理;其次,通过分析风电机组运行原理,并结合轻量梯度提升机(LightGBM)特征选择法确定风电机组正常工况预测模型的输入输出参数,并基于BiLSTM建立了高精度的风电机组正常性能预测模型;之后,利用滑窗算法构建了风电机组状态性能评价指标,并通过统计学区间估计法确定指标阈值;最后,采用风电机组真实故障数据,开展风电机组异常工况预警实验,验证了方法的有效性。

基于改进GWO-LightGBM的磨煤机故障预警方法研究

为提高燃煤电厂磨煤机运维效率、降低运维成本,对磨煤机故障预警进行了研究。创新性地提出一种基于改进灰狼优化(GWO)算法的轻量级梯度提升机(LightGBM)故障预警方法。通过建立LightGBM轴承温度预测模型获取磨煤机轴承温度阈值,并引入改进GWO算法优化模型超参数,以提高算法效率和性能。试验结果表明,改进GWO-LightGBM算法相比支持向量机(SVM)等传统算法具有更高的精度和更优的泛化能力。通过实际故障案例证明,该方法能够提前2 h对磨煤机进行早期故障预警。该方法对燃煤电厂磨煤机安全运维具有指导意义。

基于深度学习的桥梁表观裂缝检测算法研究

针对在复杂背景条件下难以直接对桥梁表观裂缝进行检测的问题,文章提出一种基于深度学习的桥梁表观裂缝检测算法。首先利用滑动窗口算法将采集到的桥梁表观裂缝图像切分为小尺寸的桥梁裂缝面元图像和桥梁背景面元图像,并根据对面元图像的分析,提出一种基于Inception网络和残差网络(ResNet)的桥梁裂缝分类模型,用于桥梁裂缝面元和桥梁背景面元的识别;然后结合桥梁裂缝分类模型与滑动窗口算法对桥梁表观裂缝图像进行检测;最后利用数字图像处理技术测量裂缝宽度。结果表明:该文算法对桥梁表观裂缝有超过99%的分类精度,可满足实际工程需要;实现了裂缝的提取并能准确地定位出裂缝在图像中的位置;根据成像原理能测量出裂缝宽度。与传统的深度学习模型相比,该模型拥有更高的执行效率,可用于大规模检测,更易于应用在桥梁健康检测中。