Abstract ：

In the intricate process of steel manufacturing, the precise measurement of molten iron is a pivotal procedure, which directly impacts the quality of steel production. Rail weighbridges are commonly deployed in the steel industry for molten iron measurement. Therefore, the accuracy of rail weighbridges weighing data is extremely important. However, due to process intricacies and equipment nuances, weighbridge weight data often exhibits outliers. This problem can potentially hinder data-centric modeling and predictive tasks. To solve this problem, this paper introduces a data cleaning methodology based on an Improved Adaptive Density-Based Spatial Clustering of Applications with Noise (IA-DBSCAN). The proposed data cleaning method is rooted in an improved adaptive DBSCAN approach, which is particularly effective in identifying clusters of arbitrary shapes and can differentiate noise from actual clusters. The application of the IA-DBSCAN algorithm facilitates the identification and cleaning of concentrated outliers. The algorithm dynamically seeks eps and MinPts parameters to achieve optimal clustering outcomes. Constraints have been incorporated into the improved algorithm, enabling it to achieve data cleaning more rapidly and accurately. After data cleaning, based on the characteristics of the selected dataset in this paper, data imputation using the linear regression method can align with the original data characteristics. Experimental results indicate that the proposed method can effectively identify and clean outliers data in the dataset, supplement missing values based on the original data characteristics, and construct a complete and anomaly-free dataset, establish a robust foundation for modeling and prediction. © 2024 IEEE.

Keyword ：

Iron and steel industry Competition Linear regression Steelmaking

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Abstract ：

Investigations into rail corrugation within metro systems have traditionally focused on specific mechanisms, thereby limiting the generalizability of proposed theories. Understanding the commonalities in rail corrugation across diverse metro lines remains pivotal for elucidating its underlying mechanisms. The present study conducted extensive field surveys and tracking tests across 14 Chinese metro lines. By employing t-distributed stochastic neighbor embedding (t-SNE) for dimensional reduction and employing the unsupervised clustering algorithm DBSCAN, the research redefines the classification of metro rail corrugation based on characteristic information. The analysis encompassed spatial distribution and temporal evolution of this phenomenon. Findings revealed that floating slab tracks exhibited the highest proportion of rail corrugation at 47%. Notably, ordinary monolithic bed tracks employing damping fasteners were more prone to inducing rail corrugation. Corrugation primarily manifested in curve sections with radii between 300 and 500 m, featuring ordinary monolithic bed track and steel-spring floating slab track structures, with wavelengths typically between 30 and 120 mm. Stick–slip vibrations of the wheel–rail system maybe led to short-wavelength corrugations (40–60 mm), while longer wavelengths (200–300 mm) exhibited distinct fatigue damage characteristics, mainly observed in steel-spring floating slab tracks and small-radius curve sections of ordinary monolithic bed tracks and ladder sleeper tracks. A classification system comprising 57 correlated features categorized metro rail corrugation into four distinct types. These research outcomes serve as critical benchmarks for validating various theories pertaining to rail corrugation formation. © The Author(s) 2024.

Keyword ：

Development law Formation mechanism Rail corrugation Metro Field test

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Abstract ：

As an emerging technological means for managing free-float bike-sharing parking, electronic fences have attracted increasing attention in major cities as a solution to the challenges posed by disorderly parking of free-float bikes. Existing research has predominantly focused on employing clustering methods from the perspectives of free-float bike-sharing companies and users to plan and deploy electronic fences. However, the results often deviate significantly from the actual phenomenon. Therefore, scientific location selection is particularly important to fully harness the effectiveness of electronic fences. This paper proposes a multiscale clustering method based on free-float bike-sharing parking features to determine the optimal locations for electronic fences. A multiobjective mixed-integer programming model is established to address the location planning problem of electronic fences, determining the planning positions, quantities, and areas of electronic fences. A case study is conducted using a local area free-float bike-sharing dataset from Shenzhen city to validate the effectiveness of the proposed method. Comparative results with traditional approaches solely relying on K-means or DBSCAN methods demonstrate that the proposed approach achieves efficient location selection, through multiscale fusion site selection in the study area of 1.5*1 km, and only 25 electronic fences need to be planned and deployed, covering a total area of 1691.88 square meters, which can provide rational placement solutions and better utilize the effectiveness of electronic fences. This method can thus offer decision-making support for the planning and location selection of electronic fences in free-float bike-sharing systems.

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Abstract ：

AIS provides a huge amount of maritime traffic data containing spatial and temporal information in a limited area. Trajectory clustering based on AIS data is a pre-task in intelligent maritime domain, providing typical movement patterns of vessels for follow-up studies in navigation safety and maritime supervision. This paper presents an AIS trajectory clustering method incorporating discrete Frechet distance and Douglas-Peucker (DP) algorithm, based on improved density-based spatial clustering of applications with noise (DBSCAN). Experimental results on the dataset of vessels entering and leaving the Taiwan Strait in November 2017 demonstrate the effectiveness of our method.

Keyword ：

vessel trajectory clustering DP Compression Unsupervised KNN plus Kneed AIS Discrete Frechet Distance

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Abstract ：

LoRa technology contributes to green energy by enabling efficient, long-range communication for the Internet of Things (IoT). This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa, where the network performance is affected by the density of gateways (GWs) and end devices (EDs), as well as environmental conditions. To mitigate interference, data throughput losses, and high-power consumption, the proposed spreading factor (SF) and hybrid (data rate|SF) models dynamically adjust the transmission parameters. The orchestration of concurrent data modifications within the network server (NS) is crucial for uninterrupted communication between GWs and EDs, especially in monitoring electric vehicle (EV) stations to reduce traffic congestion and pollution. Employing K-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms optimizes ED allocation, averts data congestion, and improves the signal-to-interference noise ratio (SINR). These methods ensure seamless information reception by meticulously allocated EDs across various GW combinations. To estimate the free-space losses (FSL), a log-distance path loss model (log-PL) is used. Exploring various bandwidths (BWs), bidirectional communications, and duty cycles (DCs) helps to prevent saturation, thus prolonging the operational lifespan of EDs. Empirical findings reveal a notable packet rejection rate (PRR) of 0% for the DBSCAN (hybrid model). In contrast, the K-means exhibits a PRR ranging from 5% (hybrid model) to 35.29% (SF model) for the ten GWs combination. Notably, the network saturation is reduced to 10.185% and 9.503%, respectively, highlighting an improvement in the average efficiency of slotted ALOHA (91.1%) and pure ALOHA (90.7%). These enhancements increase the lifespan of EDs to 15,465.27 days. © 2024

Keyword ：

K-means clustering Intelligent systems Traffic congestion Signal to noise ratio Vehicle to vehicle communications Internet of things Machine learning

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Abstract ：

To address the limitations inherent to conventional automated harvesting robots specifically their suboptimal success rates and risk of crop damage, we design a novel bot named AHPPEBot which is capable of autonomous harvesting based on crop phenotyping and pose estimation. Specifically, In phenotyping, the detection, association, and maturity estimation of tomato trusses and individual fruits are accomplished through a multi-task YOLOv5 model coupled with a detectionbased adaptive DBScan clustering algorithm. In pose estimation, we employ a deep learning model to predict seven semantic keypoints on the pedicel. These keypoints assist in the robot's path planning, minimize target contact, and facilitate the use of our specialized end effector for harvesting. In autonomous tomato harvesting experiments conducted in commercial green-houses, our proposed robot achieved a harvesting success rate of 86.67%, with an average successful harvest time of 32.46 s, showcasing its continuous and robust harvesting capabilities. The result underscores the potential of harvesting robots to bridge the labor gap in agriculture. © 2024 IEEE.

Keyword ：

Microrobots Nanorobots Risk assessment End effectors Agricultural robots Robot programming Risk perception Machine design Fruits Motion planning

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Abstract ：

本发明公开了一种枢纽站接驳出租车合乘调度匹配方法。本发明以出租车司机收益最大化以及乘客合乘里程最小为目标，将枢纽站接驳调度匹配方法分解为乘客的出行目的地方向划分及乘客的目的地选取、乘客合乘出行成本分担与乘客合乘出行路径问题，同时考虑到不同时段下的票价、绕路比例、出行时间以及载客量等约束，得到枢纽站接驳出租车合乘调度模型；然后利用DBSCAN聚类算法对乘客进行出行目的地特征选取，确定出行目的地类别相同或相近的乘客；最后利用非支配排序遗传算法对合乘路径规划问题进行求解，得到合乘出行的最优路径。本发明提高出租车司机的收益，同时提高了接运服务的质量，满足了枢纽乘客出行中多样化、多层次、个性化的需求。

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Abstract ：

针对欠定盲源分离中混合矩阵估计精度不佳的问题,本文提出了一种结合带噪声的基于密度的空间聚类(combining density-based spatial clustering of application with noise,DBSCAN)和概率密度估计的混合矩阵估计算法.首先,通过向量转换方式获得单声源时频点检测准则,并基于此准则从混合信号中检测出单声源点.其次,利用基于密度的空间聚类算法对单声源点进行聚类,由此估计出声源个数以及各类别所属的单声源点.再次,利用概率密度估计获得各类别的聚类中心,并构成混合矩阵.所提混合矩阵估计方法不需要提前设定声源个数,并且避免了由于数据分布不均所造成的聚类效果差的问题.最后,采用压缩感知技术实现源信号恢复,从而从混合信号中分离出各个声源信号.实验结果表明,本文所提的混合矩阵估计方法在声源个数未知的情况下,能够准确估计出混合矩阵;并且分离出的信号具有较高的质量.

Keyword ：

带噪声的基于密度的空间聚类 欠定盲源分离 混合矩阵估计 概率密度估计

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Sensors are critical components of heating, ventilation, and air-conditioning systems. Sensor faults can impact control regulations, resulting in an uncomfortable indoor environment and energy wastage. To detect and identify sensor faults quickly, this study proposes an enhanced principal component analysis (PCA) method using the Savitzky-Golay (SG) filter and density-based spatial clustering of applications with noise (DBSCAN) algo-rithm. First, the DBSCAN algorithm is used to automatically divide the dataset into sub-datasets with different working conditions to reduce the interference information and concentrate the information of each training set. Then, each sub-dataset is smoothed using the SG algorithm to reduce the effects of data fluctuations. The pro-cessed dataset is used to build a sub-PCA model that ultimately identifies and locates faults. The proposed strategy is validated using field operating data for 20 air-handling unit (AHU) systems, as obtained from a large commercial building. The fault detection performances of multiple strategies are compared and analysed under different degrees of bias in single AHU and multiple AHU systems. The verification results show that the pro-posed DBSCAN-SG-PCA model offers significant improvements in fault detection accuracy and fault identifica-tion sensitivity over the conventional PCA method. Compared with the SG-PCA model, the proposed model reduces the amount of data required for fault detection by an average of 13.7%, and the Youden index is increased by an average of 0.21. Furthermore, the fault detection accuracy of the proposed model is +/- 0.7 degrees C.

Keyword ：

Fault detection and diagnosis Savitzky-Golay filter Air-handling unit Principal component analysis Sensor fault Clustering

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In recent years, Light Detection and Ranging (LIDAR) has been widely used in the Intelligent Traffic System due to its high-precision data acquisition capabilities. It has great potential for accurately detecting and tracking vehicle trajectories on the roadside. This study aims to develop a novel methodological framework for accurate vehicle trajectory extraction from roadside LIDAR. The proposed method starts by designing a detection area, followed by the application of statistical filtering, density clustering, and a random sample consensus (RANSAC) segmentation algorithm to eliminate the background point cloud. Subsequently, a modified density-based spatial clustering of applications with a noise algorithm (BSO-DBSCAN) is employed to detect the vehicle based on a beetle swarm optimization algorithm. An oriented bounding box (OBB) is used to obtain vehicle position, length, and width. A modified trajectory association method based on the Kalman filtering algorithm is proposed to address vehicle occlusion. LIDAR is deployed to obtain vehicle operation data in the expressway work zone, and the performance of the proposed method is tested using an unmanned aerial vehicle (UAV). The experimental results demonstrate that the proposed method successfully extracts high-precision vehicle trajectories. Specif-ically, the vehicle recognition accuracy increased by 12.7% in MAPE and 14.9% in RMSE compared to DBSCAN. The trajectory tracking accuracy increased by 13.5%, and the number of ID switching (ID-SW) was reduced by 271 times compared to SORT. The vehicle trajectory data extracted in this study provides a foundation for traffic characteristic analysis and traffic modeling. The extracted data can be downloaded from the following GitHub: https://github.com/gao0628/Dataset.

Keyword ：

Vehicle detection Roadside LIDAR Vehicle tracking Vehicle trajectory Vehicle occlusion

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