In recent years Intelligent Transportation System (ITS) has been growing interest in the development of vehicular communication technology. The traffic in India shows considerable fluctuations owing to the static and ...
详细信息
In recent years Intelligent Transportation System (ITS) has been growing interest in the development of vehicular communication technology. The traffic in India shows considerable fluctuations owing to the static and dynamiccharacteristics of road vehicles in VANET (Vehicular Adhoc Network). These vehicles take up a convenient side lane position on the road, disregarding lane discipline. They utilize the opposing lane to overtake slower-moving vehicles, even when there are oncoming vehicles approaching. The primary objective of this study is to minimize injuries resulting from vehicle interactions in mixed trafficconditions on undivided roads. This is achieved through the implementation of the Modified Manhattan grid topology, which primarily serves to guide drivers in the correct path when navigating undivided roads. Furthermore, the fuzzy c-means algorithm (FcM) is applied to detect potential jamming attackers, while the Modified Fisheye State Routing (MFSR) algorithm is employed to minimize the amount of information exchanged among vehicles. Subsequently, the Particle Swarm Optimization (PSO) algorithm is developed to enhance the accuracy of determining the coordinates of jamming attackers within individual clusters. The effectiveness of the outcomes is affirmed through the utilization of the fuzzy c-means algorithm, showcasing a notable 30% reduction in the number of attackers, along with the attainment of a 70% accuracy rate in this research endeavor.
A few studies on urban water management have engaged in identifying Sustainable Urban Water Management (SUWM) barriers. Most of these studies proposed many strategies to address them. However, for a developing country...
详细信息
A few studies on urban water management have engaged in identifying Sustainable Urban Water Management (SUWM) barriers. Most of these studies proposed many strategies to address them. However, for a developing country like the Philippines, it is impractical to employ all of them at once, especially when resources are scarce. Furthermore, due to the system complexity, conventional analysis, which approaches SUWM barriers in isolation, may not be sufficient. With an end goal of developing leverage strategies based on the causal relationship of SUWM barriers, integration of the fuzzy Decision-Making and Trial Evaluation Laboratory (FDEMATEL), and fuzzy c-means algorithm (FcA) is proposed. The approach was employed to identify the critical SUWM barriers among a set of SUWM barriers based on their causal relationship. critical SUWM barriers were identified. Insights and proposals for addressing the critical SUWM barriers were proposed herewith to guide urban water managers.
Aiming to address the problems of low accuracy and models, a new online distance education quality evaluation model based on grey correlation algorithm is proposed. Firstly, calculate the membership of education quali...
详细信息
Aiming to address the problems of low accuracy and models, a new online distance education quality evaluation model based on grey correlation algorithm is proposed. Firstly, calculate the membership of education quality data, and complete the data collection through cluster mining. Secondly, construct the evaluation index system according to the set evaluation principles. Finally, the grey correlation relationship of education quality index data and complete the results show that the evaluation accuracy of the built model Therefore, the model has higher application value.
Indoor human action recognition, essential across various applications, faces significant challenges such as orientation constraints and identification limitations, particularly in systems reliant on non-contact devic...
详细信息
Indoor human action recognition, essential across various applications, faces significant challenges such as orientation constraints and identification limitations, particularly in systems reliant on non-contact devices. Self-occlusions and non-line of sight (NLOS) situations are important representatives among them. To address these challenges, this paper presents a novel system utilizing dual Kinect V2, enhanced by an advanced Transmission control Protocol (TcP) and sophisticated ensemble learning techniques, tailor-made to handle self-occlusions and NLOS situations. Our main works are as follows: (1) a data-adaptive adjustment mechanism, anchored on localization outcomes, to mitigate self-occlusion in dynamic orientations;(2) the adoption of sophisticated ensemble learning techniques, including a chirp acoustic signal identification method, based on an optimized fuzzyc-means-AdaBoost algorithm, for improving positioning accuracy in NLOS contexts;and (3) an amalgamation of the Random Forest model and bat algorithm, providing innovative action identification strategies for intricate scenarios. We conduct extensive experiments, and our results show that the proposed system augments human action recognition precision by a substantial 30.25%, surpassing the benchmarks set by current state-of-the-art works.
The fuzzyc-means (FcM) algorithm is one of the most widely used algorithms in unsupervised pattern recognition learning. However, real-world data is more complex and there may be some irrelevant features in the data ...
详细信息
ISBN:
(纸本)9798400707674
The fuzzyc-means (FcM) algorithm is one of the most widely used algorithms in unsupervised pattern recognition learning. However, real-world data is more complex and there may be some irrelevant features in the data that affect the final clustering results of FcM. The weighted clustering algorithm increases the importance of relevant features in the data by assigning different weights to features of different dimensions, and at the same time weakens the influence of irrelevant features on the clustering results. However, both the weighted clustering algorithm and the FcM algorithm will have classification errors as the observation noise increases. As a distance measure between two distributions, relative entropy is added to the objective function as a regularization function, which can minimize the distance within the cluster and maximize the difference between clusters. Therefore, this paper proposes a new feature-weighted relative entropy clustering algorithm (REFcM_EW). The REFcM_EW algorithmcombines feature weight and relative entropy, which not only enhances the importance of relevant features in the data but also has better noise detection capability. Experimental results show that REFcM_EW has a good effect on the strip data.
Estimating closed curves based on noisy data has been a popular and yet a challenging problem in many fields of applications. Yet, uncertainty quantification of such estimation methods has received much less attention...
详细信息
Estimating closed curves based on noisy data has been a popular and yet a challenging problem in many fields of applications. Yet, uncertainty quantification of such estimation methods has received much less attention in the literature. The primary challenge stems from the fact that the parametrization of a closed curve is not generally unique and hence popular curve fitting methods (e.g., weighted least squares based on known parametrization) does not work well due to initialization instabilities leading to larger uncertainties. First, an initial set of cluster points are obtained by means of a constrained fuzzy c-means algorithm and an initial curve is constructed by fitting a B-spline curve based on the cluster centers. Second, a novel tuning parameter selection procedure is proposed to obtain optimal number of knots for the B-spline curve. Experimental results with simulated noisy data show that the proposed method works well for a variety of unknown closed curves with sharp changes of slopes and complex curvatures, even when moderate to large noises are added with heteroskedastic errors. Finally, a new curvature preserving uncertainty quantification method is proposed based on an adaptation of bootstrap method that provides confidence band around the fitted curve, an aspect that is rarely provided by popular curve fitting methods.
The Teaching and Learning Optimization algorithm (TLBO) simulates the two main stages of traditional classroom teaching. It is divided into the "teaching stage" and the "learning stage". However, t...
详细信息
ISBN:
(纸本)9798400709098
The Teaching and Learning Optimization algorithm (TLBO) simulates the two main stages of traditional classroom teaching. It is divided into the "teaching stage" and the "learning stage". However, the traditional teaching process is a relatively complex process, and the simulation process of this algorithm is relatively simple. Therefore, this study introduces two new educational concepts and educational methods, "group teaching" and "adaptive learning", into the simulation process of traditional classroom teaching, and proposed an improved teaching-based optimization algorithm (TLBO-Adam) based on adaptive moment estimation. This study introduces group teaching in the "teaching stage" and uses fuzzyc-meansclustering (FcM) algorithm in the field of machine learning, and introduces Adaptive learning in the "learning stage" and uses an adaptive moment estimation algorithm with the characteristics of a goal-oriented mechanism to solve the problem of premature convergence and slow convergence of the TLBO algorithm. Finally, through designing comparative experiments, experimental results show that the improved algorithm TLBO-Adam is effective in 9 test functions and can find the global optimal solution quickly and efficiently compared with other swarm intelligence algorithms.
Lane changing behavior generally expresses uncertainty due to the impact of environmental factors, and unreasonable lane changes can cause serious collisions. High precision prediction of lane changing intent is helpf...
详细信息
Lane changing behavior generally expresses uncertainty due to the impact of environmental factors, and unreasonable lane changes can cause serious collisions. High precision prediction of lane changing intent is helpful to enhance proactivity in driving safety protection. This study proposed a lane-changing prediction model based on fuzzyc-meansclustering algorithm and adaptive Neural Network (FcMNN), which introduced a new prediction process: (1) Unsupervised learning method: categorize original dataset into different clusters according to their distribution features;(2) Supervised learning method: optimize sub Neural Network structures and weighting parameters for each cluster or pattern. Through comparing with several traditional methods under different simulation scenarios, the proposed model effectively improve the prediction performance and stability. The results obtained in this study will be helpful to deeply analyze the intent recognition of driving behavior, improve the safety of lane-changing behavior, and provide key technology in driving prediction of Advanced Driver Assistance System (ADAS). (c) 2019 Elsevier Ltd. All rights reserved.
To inform the power utility and users, and help them reduce the huge financial losses due to voltage sag, it is important to obtain information on voltage sag events in advance. This paper proposes a method for predic...
详细信息
To inform the power utility and users, and help them reduce the huge financial losses due to voltage sag, it is important to obtain information on voltage sag events in advance. This paper proposes a method for predicting voltage sag characteristics based on fuzzy time series. First, we propose a homologous aggregation method to eliminate redundant data representing the same disturbance event and obtain the time series of voltage sag (TSOVS), which can describe the trend of the voltage sag data. Second, this paper introduces a fuzzification method for the time series of voltage sag based on the fuzzy c-means algorithm (FcMA), which transforms the time series of voltage sag into a fuzzy time series composed of interval symbols, to characterize the mapping relationship between the disturbance and voltage sag event. Furthermore, a hidden Markov model (HMM) of voltage sag is constructed to reveal the transformation relationship among elements in the fuzzy time series, considering the causal relationship between the disturbance and voltage sag event. Finally, the occurrence time and residual voltage of the voltage sag in the future were predicted based on this transformation relation. The measured voltage sags in a province in central china were used to verify the accuracy of the proposed method, prediction results with an accuracy of up to 90%.
fuzzy c-means algorithm (Fcm) frequently applid in machine learning has been proven an effective clustering approach. However, the traditional Fcm cannot distinguish the importance of the different data objects and th...
详细信息
fuzzy c-means algorithm (Fcm) frequently applid in machine learning has been proven an effective clustering approach. However, the traditional Fcm cannot distinguish the importance of the different data objects and the discriminative ability of the different features in the clustering process. In this paper, we propose a new kind of Fcm clustering framework: *** the different data weights and feature weights, an adaptive data weights vector and an adaptive feature weights matrix are introduced into the conventional Fcm and a new objective function is constructed. By the proposed objective function, the corresponding scientific updating iterative rules of the membership matrix, the weights of the different feature, the weights of the different data object and the cluster centers can be derived *** results have demonstrated that the algorithm proposed in this paper can deliver consistently promising results and improve the clustering performance greatly.
暂无评论