Density-based Method Research Articles

An Evolutionary Algorithm with Clustering-Based Assisted Selection Strategy for Multimodal Multiobjective Optimization

In multimodal multiobjective optimization problems (MMOPs), multiple Pareto optimal sets, even some good local Pareto optimal sets, should be reserved, which can provide more choices for decision-makers. To solve MMOPs, this paper proposes an evolutionary algorithm with clustering-based assisted selection strategy for multimodal multiobjective optimization, in which the addition operator and deletion operator are proposed to comprehensively consider the diversity in both decision and objective spaces. Specifically, in decision space, the union population is partitioned into multiple clusters by using a density-based clustering method, aiming to assist the addition operator to strengthen the population diversity. Then, a number of weight vectors are adopted to divide population into N subregions in objective space (N is population size). Moreover, in the deletion operator, the solutions in the most crowded subregion are first collected into previous clusters, and then the worst solution in the most crowded cluster is deleted until there are N solutions left. Our algorithm is compared with other multimodal multiobjective evolutionary algorithms on the well-known benchmark MMOPs. Numerical experiments report the effectiveness and advantages of our proposed algorithm.

A new density-based subspace selection method using mutual information for high dimensional outlier detection

Outlier detection in high dimensional data faces the challenge of curse of dimensionality, where irrelevant features may prevent detection of outliers. In this research, we propose a novel efficient unsupervised density-based subspace selection for outlier detection in the projected subspace. First, the Maximum-Relevance-to-Density algorithm(MRD) is proposed to select the relevant subspace based on the mutual information. Then, applying the concept of redundancy among features, we present an efficient relevant subspace selection method called minimum-Redundancy-Maximum-Relevance-to-Density (mRMRD). Finally, the degree of outlierness of data points in the corresponding relevant subspace is computed based on Local Outlier Factor(LOF). Experimental results on both real and synthetic data demonstrate that the proposed algorithms – based on MRD and mRMRD criteria – increase the accuracy of outlier detection while reducing computational complexity and execution time. Moreover, as the dimensionality increases, the accuracy of outlier detection on mRMRD-based relevant subspace is higher than MRD-based relevant subspace. This verifies that the proposed mRMRD-based subspace selection algorithm can efficiently select the subspace by considering the relevance between features.

Topology optimization and fatigue analysis of a lifting hook

In this study, a lifting hook has been redesigned with topology optimization and fatigue analysis has been done. With a density-based method, volume minimization has been achieved. All processes have been realized with the help of finite element method implemented in a commercial software. A standard lifting hook model has been used in the process. After optimization, the standard lifting hook was remodeled with CAD software. Then the new model was analyzed. This process has been repeated for three different models. On the basis of the obtained results, the topology optimization undertaken within the scope of the study demonstrates that it is possible to redesign a lifting hook with reduced weight and at the same time to satisfy the required strength properties.

Room Geometry Estimation Using the Multipath Delays

This work proposes a method to acquire the geometry of a room in terms of the size and shape by exploiting the signal propagation times of the single bound reflections (SBRs). The information of room geometry is crucial for improving localization and assisting robot navigation under complex indoor environments. The data association problem for the SBR time measurements from the same reflection surface is addressed by the minimal measurement estimator (MME) together with the density-based clustering method. A best linear unbiased estimator (BLUE) is derived to integrate the MMEs of the same surface for the room geometry estimation. Analysis shows and simulation validates that the proposed method achieves the Cramèr-Rao Lower Bound (CRLB) performance for Gaussian data model.

How morphology shapes the parameter sensitivity of lake ecosystem models

A global sensitivity analysis of a lake ecosystem model (GOTM-FABM-PCLake) was undertaken to test the impacts of lake morphology on parameter sensitivity in three different lakes. The analysis was facilitated by the Parallel Sensitivity and Auto-Calibration tool (parsac) and included a screening step with the density-based Borgonovo's method followed by in-depth analysis with both Borgonovo's and the variance-based Sobol’ methods. The Borgonovo's method proved efficient in ranking the most influential parameters and its results were corroborated by the Sobol’ method. For total phosphorus and total nitrogen, parameters related to the benthic-pelagic coupling and phytoplankton, were particularly important for the shallower lakes, whereas the most important parameters for total nitrogen were related mainly to the benthic-pelagic coupling in the deepest lake. For chlorophyll a, phytoplankton and zooplankton parameters were most influential. We conclude that lake morphology shapes the parameter sensitivity of lake ecosystem models.

Automated rebar diameter classification using point cloud data based machine learning

Inspecting the diameter and spacing of rebar is an important task conducted by fabricators and site engineers during the manufacturing and construction stages. This is because the bearing capacity of reinforced concrete structures is affected by the size and position of the rebar, so installing rebar of the correct size and position should be ensured to safeguard the structural integrity of the structure. This study presents a new terrestrial laser scanning (TLS)-based method using machine learning to automatically classify rebar diameters and accurately estimate rebar spacing. To this end, a new methodology, named density based machine model, is proposed to improve classification accuracy. To validate the proposed method, experimental tests on laboratory specimens with rebars of seven different diameters are conducted. The results show that the prediction accuracy for large rebar diameters measuring D25-D40 are up to 97.2%, demonstrating great potential for the application of the proposed technique on manufacturing and construction sites. The key findings of the study are: (1) the proposed density-based modeling method for rebar diameter prediction is superior to the traditional machine learning approach; (2) scan density is one of the most important factors in the prediction results, especially in the small rebar diameter group; and (3) it was found that at least 10 points/cm2 is necessary to ensure accurate rebar diameter classification in small rebar diameters between D10-D20. It is expected that the proposed rebar diameter and rebar spacing technique will be useful in providing autonomous and accurate rebar inspection in manufacturing factories and on construction sites.

Analysis of measure employee performance using EDAS Method

Employee performance in EDAS (Estimation average solution) based on the method.An effective performance management strategy motivates actions to support big picture goals and improve employee performance. Outline expectations and goals Create, provide feedback, evaluate results, and develop growth opportunities Leaders can use the Create process.Employee productivity is as the amount of work performed by an employee over a period of time is defined. As a manager, give your teammates their best Empower yourself to help you get the job done has you also have a responsibility. Employee three ways to measure productivity include: Measuring goals. Measuring the quality of work.Alternative: Communication skills, technical skills, analytical skills, creative skills. Assessment Option: With colleagues Communication, emphasis on friendship, openness with co-workers and other employees Respect and provide an environment conducive to expressing opinions, Density-based method. It is solved by using the EDAS method. It is the data set of this paper.From the result it is seen that Density-based method and is got the first rank whereas is the providing an environment conducive to expression of ideasgot is having the lowest rank.In this paper the result it is seen that Density-based method and is got the first rank whereas is the providing an environment conducive to expression of ideasgot is having the lowest rank.

Toward Translating Raw Indoor Positioning Data into Mobility Semantics

Indoor mobility analyses are increasingly interesting due to the rapid growth of raw indoor positioning data obtained from IoT infrastructure. However, high-level analyses are still in urgent need of a concise but semantics-oriented representation of the mobility implied by the raw data. This work studies the problem of translating raw indoor positioning data into mobility semantics that describe a moving object’s mobility event (What) someplace (Where) at some time (When). The problem is non-trivial mainly because of the inherent errors in the uncertain, discrete raw data. We propose a three-layer framework to tackle the problem. In the cleaning layer, we design a cleaning method that eliminates positioning data errors by considering indoor mobility constraints. In the annotation layer, we propose a split-and-match approach to annotate mobility semantics on the cleaned data. The approach first employs a density-based splitting method to divide positioning sequences into split snippets according to underlying mobility events, followed by a semantic matching method that makes proper annotations for split snippets. In the complementing layer, we devise an inference method that makes use of the indoor topology and the mobility semantics already obtained to recover the missing mobility semantics. The extensive experiments demonstrate that our solution is efficient and effective on both real and synthetic data. For typical queries, our solution’s resultant mobility semantics lead to more precise answers but incur less execution time than alternatives.

Anomaly Detection and Severity Prediction of Air Leakage in Train Braking Pipes

Air leakage in braking pipes is a commonly encountered mechanical defect on trains. A severe air leakage will lead to braking issues and therefore decrease the reliability and cause train delays or stranding. However, air leakage is difficult to be detected via visual inspection and therefore most air leakage defects are run to fail. In this research we present a framework that not only can detect air leakages but also predicts the severity of air leakages so that action plans can be determined based on the severity level. The proposed contextual anomaly detection method detects air leakages based on the on/off logs of a compressor. Air leakage causes failure in the context when the compressor idle time is short than the compressor run time, that is, the speed of air consumption is faster than air generation. In our method the logistic regression classifier is adopted to model two different classes of compressor behavior for each train separately. The logistic regression classifier defines the boundary separating the two classes under normal situations and models the distribution of the compressor idle time and run time separately using logistic functions. The air leakage anomaly is further detected in the context that when a compressor idle time is erroneously classified as a compressor run time. To distinguish anomalies from outliers and detect anomalies based on the severity degree, a density-based clustering method with a dynamic density threshold is developed for anomaly detection. The results have demonstrated that most air leakages can be detected one to four weeks before the braking failure and therefore can be prevented in time. Most importantly, the contextual anomaly detection method can pre-filter anomaly candidates and therefore avoid generating false alarms. To facilitate the decisionmaking process, the logistic function built on the compressor run time is further used together with the duration of an air leakage to model the severity of the air leakage. By building the prediction model on the severity, the remaining useful life of the air braking pipe until it reaches a certain level of severity can be estimated.

Topology-Optimized Omnidirectional Broadband Acoustic Ventilation Barrier

As a problem in acoustics, sound insulation finds wide applications in diverse situations. We design and experimentally implement an omnidirectional broadband acoustic ventilation barrier using a topology-optimization method. We propose a broadband acoustic barrier constructed of interlaced contraction structure with a central hollow channel, utilizing Bragg's scattering mechanism happening at impedance discontinuities. Based on this barrier, a density-based topology-optimization method is employed for the inverse design to broaden the sound-insulation band nearly 90% while keeping a low transmission. The topology-optimization method systematically optimizes the scatterers distributed at intervals in our initial barrier in dimensions of size, shape, and orientation to possess many complicated features, which produces the maximum reflection at a desired frequency range. Experiments with acoustic waves of different incident angles are conducted to validate the optimized design, whose results are consistent with the simulations. The measured ventilation rate of the optimized barrier reaches 60.8% compared to initial barrier (49.2%) due to the reduction of fill rate of solid material and aerodynamic loss, demonstrating a high ventilation effect. Our design opens routes to design sound insulators that enable applications to be air permeable yet sound proofing simultaneously.

Multi-scale topology optimization with shell and interface layers for additive manufacturing

Abstract This paper contributes a multi-scale topology optimization method for lattice structure design with both shell and lattice-lattice interface layers to enhance the structural aesthetical, mechanical, and manufacturability properties. To improve the robustness, a multi-step algorithm is proposed. The variable thickness sheet (VTS) method and the projection-based regularization mechanism are used to realize the free material optimization with a limited number of lattice microstructures. Then, a series of successive filtering and projection operations are adopted to identify the lattice-lattice interface layers based on the material density distribution. Elements with close densities are grouped into clusters and for elements inside each cluster, they are assumed to have the same periodical lattice microstructure. In the second step, based on the achieved discrete material distribution, the macrostructure with a solid shell layer and the multiple representative lattice microstructures are concurrently optimized with the density-based multi-scale topology optimization method. Several benchmark numerical examples are studied to demonstrate the effectiveness of the proposed method. Finally, additive manufacturing experiments are performed to validate the stiffness enhancement by including the lattice-lattice interface layers.

Clustering-based concurrent topology optimization with macrostructure, components, and materials

This paper proposes a clustering-based concurrent topology optimization method for designing additive manufacturable cellular structures. In the context of multiscale topology optimization, the macrodesign domain can be divided into several subdomains (components) to reduce the number of microstructures which are needed to be optimized. However, in previous works, the division pattern is either artificially decided and fixed during the iteration process or updated according to a fixed parameter range. In this paper, a dynamic clustering strategy is developed to automatically divide the macrodesign domain into several subdomains according to the directions and ratio of the principal stress. K-means algorithm is adopted here for clustering; the advantages are that no predefined range is needed to group the microstructures and the number of microstructures used in the optimization can be arbitrarily specified. The macrostructure and the representative microstructures are optimized simultaneously using a density-based method. Each iteration consists of three sequential steps: First, the macrovariables are updated one step forward. Then, get the stress tensors from the macrovariables updating step and perform the clustering analysis. In the end, update the microvariables one step forward under the current clustering pattern and start the next iteration until the optimization converges. Several numerical examples are presented to demonstrate the effectiveness of the proposed method.

Toward the implementation of a multi-component framework in a density-based flow solver for handling chemically reacting flows

PurposeThe purpose of this study is to present and demonstrate a numerical method for solving chemically reacting flows. These are important for energy conversion devices, which rely on chemical reactions as their operational mechanism, with heat generated from the combustion of the fuel, often gases, being converted to work.Design/methodology/approachThe numerical study of such flows requires the set of Navier-Stokes equations to be extended to include multiple species and the chemical reactions between them. The numerical method implemented in this study also accounts for changes in the material properties because of temperature variations and the process to handle steep spatial fronts and stiff source terms without incurring any numerical instabilities. An all-speed numerical framework is used through simple low-dissipation advection upwind splitting (SLAU) convective scheme, and it has been extended in a multi-component species framework on the in-house density-based flow solver. The capability of solving turbulent combustion is also implemented using the Eddy Dissipation Concept (EDC) framework and the recent k-kl turbulence model.FindingsThe numerical implementation has been demonstrated for several stiff problems in laminar and turbulent combustion. The laminar combustion results are compared from the corresponding results from the Cantera library, and the turbulent combustion computations are found to be consistent with the experimental results.Originality/valueThis paper has extended the single gas density-based framework to handle multi-component gaseous mixtures. This paper has demonstrated the capability of the numerical framework for solving non-reacting/reacting laminar and turbulent flow problems. The all-speed SLAU convective scheme has been extended in the multi-component species framework, and the turbulent model k-kl is used for turbulent combustion, which has not been done previously. While the former method provides the capability of solving for low-speed flows using the density-based method, the later is a length-scale-based method that includes scale-adaptive simulation characteristics in the turbulence modeling. The SLAU scheme has proven to work well for unsteady flows while the k-kL model works well in non-stationary turbulent flows. As both these flow features are commonly found in industrially important reacting flows, the convection scheme and the turbulence model together will enhance the numerical predictions of such flows.

A Deep Neural Network for Crossing-City POI Recommendations

With the popularity of location-aware devices (e.g., smart phones), large amounts of location-based social media data such as check-ins are generated. This stimulates plenty of studies for POI recommendations by applying machine learning techniques. However, most of the existing studies focus on POI recommendations in the same city or region, and fail to recommend POIs for users when they travel to a new city. In this paper, we propose a novel deep neural network, named as ST-TransRec, for crossing-city POI recommendations. It integrates the deep neural network, transfer learning technique, and density-based resampling method into a unified framework. In this model, the deep neural network is used to capture users’ preferences for POIs and learn the embeddings of POIs. Besides, the transfer learning technique is employed to bridge the gap between cities that results from the city-dependent features. As the distributions over POIs are imbalanced, we design a density-based spatial resampling model which enables POIs to be well matched across cities. We conduct extensive experiments on two real-world datasets. The experimental results show the advantages of ST-TransRec over the state-of-the-art methods for crossing-city POI recommendations.

Low-complexity point cloud denoising for LiDAR by PCA-based dimension reduction

Signals emitted by LiDAR sensors would often be negatively influenced during transmission by rain, fog, dust, atmospheric particles, scattering of light and other influencing factors, causing noises in point cloud images. To address this problem, this paper develops a new noise reduction method to filter LiDAR point clouds, i.e. an adaptive clustering method based on principal component analysis (PCA). Different from the traditional filtering methods that directly process three-dimension (3D) point cloud data, the proposed method uses dimension reduction to generate two-dimension (2D) data by extracting the first principal component and the second principal component of the original data with little information attrition. In the 2D space spanned by two principal components, the generated 2D data are clustered for noise reduction before being restored into 3D. Through dimension reduction and the clustering of the generated 2D data, this method derives low computational complexity, effectively removing noises while retaining details of environmental features. Compared with traditional filtering algorithms, the proposed method has higher precision and recall. Experimental results show a F-score as high as 0.92 with complexity reduced by 50% compared with traditional density-based clustering method.

Varying density method for data stream clustering

In this paper, a new online-offline density-based clustering method for data stream with varying density is proposed. In the online phase, the summary of data is created (often known as micro-clusters) and in the offline phase, this synopsis of data is used to form the final clusters. Finding the accurate micro-clusters is the goal of online phase. When a new data point arrives, the procedure of finding the nearest and best fit micro-cluster is the time consuming process. This procedure can lead to increase the execution time. To address this problem, a new merging algorithm is proposed. For maintaining a limited number of micro-clusters, a pruning process is applied along with the summarization process. In the existing methods, this pruning process takes too long time to remove micro-clusters whose do not receive objects frequently that cause to increase the memory usage. In this paper, to solve this problem, a new pruning algorithm is introduced. Another problem with density-based methods is that they use global parameters in the data sets with varying density that can lead to dramatic decrease in the clustering quality. In our work, to create final clusters, a new density-based algorithm that works based on only MinPts parameter is proposed for increasing the clustering quality of data sets with varying density. The performance evaluation on both synthetic and real data sets illustrates the efficiency and effectiveness of the proposed method. The experimental results show that our method can increase the clustering quality in data sets with varying density along with limited time and memory usage.

Cluster-based multi-objective optimization for identifying diverse design options: Application to water resources problems

In this study, a novel density-based spatial clustering method is developed to maintain a diverse set of solutions for stochastic multi-objective optimization algorithms. This method dynamically clusters solutions in the decision space after solutions evaluations. Dominance check is localized to maintain solutions that are globally dominated but locally non-dominated in their cluster. Unlike the original solution archiving, the proposed method implemented for Pareto Archived-Dynamically Dimensioned Search successfully finds optimal and near-optimal fronts with different cluster labels in two mathematical case studies. Two environmental benchmark problems are also solved and a three-stage screening process is applied to their archive sets to identify the number of dissimilar options. The dissimilarity index devised for this study shows a significantly higher distinction level and archive size for the cluster-based solution archiving, which allows decision-makers to have higher flexibility in refining their preferences for robust decision-making in the environmental problems, compared with the original archiving.

Data-Driven Trajectory Quality Improvement for Promoting Intelligent Vessel Traffic Services in 6G-Enabled Maritime IoT Systems

Future generation communication systems, such as 5G and 6G wireless systems, exploit the combined satellite-terrestrial communication infrastructures to extend network coverage and data throughput for data-driven applications. These ground-breaking techniques have promoted the rapid development of Internet of Things (IoT) in maritime industries. In maritime IoT applications, intelligent vessel traffic services can be guaranteed by collecting and analyzing high volume of spatial data flows from automatic identification system (AIS). This AIS system includes a highly integrated automatic equipment, including functionalities of core communication, tracking, and sensing. The increased utilization of shipboard AIS devices allows the collection of massive trajectory data. However, the received raw AIS data often suffers from undesirable outliers (i.e., poorly tracked timestamped points for vessel trajectories) during signal acquisition and analog-to-digital conversion. The degraded AIS data will bring negative effects on vessel traffic services (e.g., maritime traffic monitoring, intelligent maritime navigation, vessel collision avoidance, etc.) in maritime IoT scenarios. To improve the quality of vessel trajectory records from AIS networks, we propose to develop a two-phase data-driven machine learning framework for vessel trajectory reconstruction. In particular, a density-based clustering method is introduced in the first phase to automatically recognize the undesirable outliers. The second phase proposes a bidirectional long short-term memory (BLSTM)-based supervised learning technique to restore the timestamped points degraded by random outliers in vessel trajectories. Comprehensive experiments on simulated and realistic data sets have verified the dominance of our two-phase vessel reconstruction framework compared to other competing methods. It thus has the capacity of promoting intelligent vessel traffic services in 6G-enabled maritime IoT systems.

Topology design of two-fluid heat exchange

Heat exchangers are devices that typically transfer heat between two fluids. The performance of a heat exchanger such as heat transfer rate and pressure loss strongly depends on the flow regime in the heat transfer system. In this paper, we present a density-based topology optimization method for a two-fluid heat exchange system, which achieves a maximum heat transfer rate under fixed pressure loss. We propose a representation model accounting for three states, i.e., two fluids and a solid wall between the two fluids, by using a single design variable field. The key aspect of the proposed model is that mixing of the two fluids can be essentially prevented. This is because the solid constantly exists between the two fluids due to the use of the single design variable field. We demonstrate the effectiveness of the proposed method through three-dimensional numerical examples in which an optimized design is compared with a simple reference design, and the effects of design conditions (i.e., Reynolds number, Prandtl number, design domain size, and flow arrangements) are investigated.

Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography

Diabetic retinopathy (DR) is a severe retinal disorder that can lead to vision loss, however, its underlying mechanism has not been fully understood. Previous studies have taken advantage of Optical Coherence Tomography (OCT) and shown that the thickness of individual retinal layers are affected in patients with DR. However, most studies analyzed the thickness by calculating summary statistics from retinal thickness maps of the macula region. This study aims to apply a density function-based statistical framework to the thickness data obtained through OCT, and to compare the predictive power of various retinal layers to assess the severity of DR. We used a prototype data set of 107 subjects which are comprised of 38 non-proliferative DR (NPDR), 28 without DR (NoDR), and 41 controls. Based on the thickness profiles, we constructed novel features which capture the variation in the distribution of the pixel-wise retinal layer thicknesses from OCT. We quantified the predictive power of each of the retinal layers to distinguish between all three pairwise comparisons of the severity in DR (NoDR vs NPDR, controls vs NPDR, and controls vs NoDR). When applied to this preliminary DR data set, our density-based method demonstrated better predictive results compared with simple summary statistics. Furthermore, our results indicate considerable differences in retinal layer structuring based on the severity of DR. We found that: (a) the outer plexiform layer is the most discriminative layer for classifying NoDR vs NPDR; (b) the outer plexiform, inner nuclear and ganglion cell layers are the strongest biomarkers for discriminating controls from NPDR; and (c) the inner nuclear layer distinguishes best between controls and NoDR.