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Optimization on Multimodal Network Considering Time Window Under Uncertain Demand

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Bei, Honghan Lin, Han Yang, Fangjiao Li, Xiaolong Murcio, Roberto Yang, Tianren Dalian Maritime Univ Sch Maritime Econ & Management Dalian 116026 Peoples R China Dalian Maritime Univ Collaborat Innovat Ctr Transport Studies Dalian 116026 Peoples R China COSCO SHIPPING Logist & Supply Chain Management Co Dalian 116001 Peoples R China Birkbeck Univ London Sch Social Sci London WC1E 7HX England UCL Ctr Adv Spatial Anal London W1T 4TJ England Univ Hong Kong Dept Urban Planning & Design Hong Kong Peoples R China

Improving transport efficiency is challenging for multimodal transport participants to improve cost-effectiveness. This paper proposes to select city nodes and establish a multi-objective fuzzy optimization model with mixed time window constraints to consider customer demand and transportation time uncertainty. T-rex Optimization algorithm (TROA) is used to solve the problem, which efficiently lowers transportation costs and carbon emissions and has higher precision and dependability than Particle Swarm Optimization (PSO) and Genetic algorithm (GA). The efficacy of this method is proven using the example of the multimodal transportation network in China's central-eastern economic zone. These findings provide potential solutions for multimodal transportation aimed at enhancing transportation efficiency.

关键词： Transportation Optimization Uncertainty Planning Costs Routing Carbon dioxide Optimization models Logistics Biological system modeling clustering algorithm mixed time windows multimodal transport network optimization uncertain demand

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Unsupervised few shot learning architecture for diagnosis of periodontal disease in dental panoramic radiographs

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Kim, Min Joo Chae, Sun Geu Bae, Suk Joo Hwang, Kyung-Gyun Hanyang Univ Dept Med & Digital Engn Seoul 04763 South Korea Hanyang Univ Dept Ind Engn Seoul 04763 South Korea Hanyang Univ Dept Dent Coll Med Seoul 04763 South Korea

In the domain of medical imaging, the advent of deep learning has marked a significant progression, particularly in the nuanced area of periodontal disease diagnosis. This study specifically targets the prevalent issue of scarce labeled data in medical imaging. We introduce a novel unsupervised few-shot learning algorithm, meticulously crafted for classifying periodontal diseases using a limited collection of dental panoramic radiographs. Our method leverages UNet architecture for generating regions of interest (RoI) from radiographs, which are then processed through a Convolutional Variational Autoencoder (CVAE). This approach is pivotal in extracting critical latent features, subsequently clustered using an advanced algorithm. This clustering is key in our methodology, enabling the assignment of labels to images indicative of periodontal diseases, thus circumventing the challenges posed by limited datasets. Our validation process, involving a comparative analysis with traditional supervised learning and standard autoencoder-based clustering, demonstrates a marked improvement in both diagnostic accuracy and efficiency. For three real-world validation datasets, our UNet-CVAE architecture achieved up to average 14% higher accuracy compared to state-of-the-art supervised models including the vision transformer model when trained with 100 labeled images. This study not only highlights the capability of unsupervised learning in overcoming data limitations but also sets a new benchmark for diagnostic methodologies in medical AI, potentially transforming practices in data-constrained scenarios.

关键词： Artificial intelligence clustering algorithm Convolutional variational autoencoder (CVAE) Deep learning UNet

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Improvement of clustering algorithm for WSNs

Improvement of Clustering Algorithm for WSNs

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International Conference on Application of Materials Science and Environmental Materials (AMSEM)

作者： Chen, QiGong Wang, YongZhi Wei, LiSheng Gao, WenGen Anhui Polytech Univ Anhui Prov Detect Lab Wuhu 241000 Anhui Peoples R China

ISBN: (纸本)9783038352709

Energy consumption is a hot issue in WSNs (Wireless Sensor Networks). In this paper, we present an improved clustering algorithm. By changing the order of traditional WSNs clustering algorithm, this algorithm uses k-means clustering firstly base on optimal number of cluster head is determined;Then selects cluster head by an improved LEACH (Low Energy Adaptive clustering Hierarchy) algorithm;Finally, Our experimental results demonstrate that this approach can reduces energy consumption and increases the lifetime of the WSNs.

关键词： energy consumption WSNs k-means LEACH clustering algorithm

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A clustering algorithm Applying to Transportation Market Segment

A Clustering Algorithm Applying to Transportation Market Seg...

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3rd International Conference on Civil Engineering and Transportation (ICCET 2013)

作者： Jing, Chongyi Sun, Hong Zhang, Peiwen Civil Aviat Flight Univ China Air Transportat Management Sch Guanghan 618307 Sichuan Peoples R China

ISBN: (纸本)9783038350064

An integration algorithm for clustering is presented, in which a maximization & minimum algorithm to determine the initial centers and BWP (Between-Within Proportion) index for input of optimal k. In theory, the bigger the BWP index, the better the clustering effectiveness. Then a numerical example of air transport market segment is presented to show the effectiveness and efficiency of the method presented in the document.

关键词： clustering algorithm Between-Within Proportion K-Means algorithm Transportation Market Segment

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A NONLINEAR CONVOLUTIONAL NEURAL NETWORK algorithm FOR AUTONOMOUS VEHICLE LANE LINE DETECTION

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第5期25卷 4237-4245页

作者： Lyu, Kanhui Zhejiang Financial Coll Inst Informat Technol Hangzhou 310018 Zhejiang Peoples R China

The traditional lane line detection algorithm relies on artificial design features, which has poor robustness and cannot cope with the complex urban street background. With the rise of deep learning technology, the algorithm model with convolutional neural network as the mainstream is widely used in the field of computer vision, which provides a new idea for lane line detection. In order to improve the disadvantages of traditional lane line detection methods that are vulnerable to environmental impact and poor robustness, a nonlinear convolution neural network algorithm for driverless lane line detection is proposed. Firstly, the pretreatment method of extracting the region of interest and enhancing the contrast of lane lines is used to reduce the unnecessary image background and enhance the feature details of the image. Existing deep learning-based lane line detection algorithms still have difficulties. First, accumulated wear and tear will cause lane line to fade and fade;roadside trees and buildings can interfere with the performance of lane line detection algorithm. In addition, compared with the pixels of the whole picture, the lane line pixels are too few, and the deep convolution neural network of layer convolution is easy to lead to the loss of details. In addition, when the traffic flow is large, the lane line is easily blocked, which makes it more difficult to detect the lane line. Then the model is built based on the lane line image features extracted by CNN, and the DBSCAN clustering algorithm is used to post-process the lane line segmentation model;Finally, the least square method is used to fit the quadratic curve of the pixel peak points of the lane line, and the fitting results are regressed to the original image. The experimental results show that the accuracy and recall of the lane line detection model verification set are 91.3% and 90.6%, respectively, indicating that the model has a good segmentation effect. It is proved that the lane line detect

关键词： Lane line detection Convolution neural network Deep learning clustering algorithm

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Reliability analysis of railway assets considering the impact of geographical and climatic properties

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年 1-14页

作者： Kasraei, Ahmad Garmabaki, A. H. S. Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Div Operat & Maintenance S-97187 Lulea Sweden

Various factors, including climate change and geographical features, contribute to the deterioration of railway infrastructures over time. The impacts of climate change have caused significant damage to critical components, particularly switch and crossing (S&C) elements in the railway network. These components are sensitive to abnormal temperatures, snow and ice, and flooding, making them susceptible to failures. The consequences of S&C failures can have a detrimental effect on the reliability and safety of the entire railway *** is crucial to have a reliable clustering of railway infrastructure assets based on various climate zones to make informed decisions for railway network operation and maintenance in the face of current and future climate scenarios. This study employs machine learning models to categorize S & Cs;therefore, historical maintenance data, asset registry information, inspection data, and weather data are leveraged to identify patterns and cluster failures. The analysis reveals four distinct clusters based on climatic patterns. The effectiveness of the proposed model is validated using S&C data from the Swedish railway *** utilizing this clustering approach, the whole of Sweden railway network divided into 4 various groups. Utilizing this groups the development of model can associated with enhancing certainty of decision-making in railway operation and maintenance management. It provides a means to reduce uncertainty in model building, supporting robust and reliable decision-making. Additionally, this categorization supports infrastructure managers in implementing climate adaptation actions and maintenance activities management, ultimately contributing to developing a more resilient transport infrastructure.

关键词： Climate zones clustering algorithm Railway asset Reliability analysis

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Occlusion target recognition algorithm based on improved YOLOv4

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第6期24卷 3799-3811页

作者： Jia, Fengguang Shandong Jiaotong Univ Coll Naval Architecture & Port Engn Weihai 264200 Peoples R China

With the development of deep learning, recognition algorithms are increasingly widely used in various fields, and face recognition is a technological embodiment of recognition algorithms in real life. Due to the limited recognition range, the face may be occluded, so it is necessary to design an occluded target recognition algorithm model. This article aims to optimize the "You Only Look Once Version 4" algorithm and propose an improved occlusion target recognition algorithm model by introducing separable convolutional optimization and embedding attention mechanism. This paper designed relevant experiments to verify the model performance and compared the facial recognition model designed by MM Goyani. The experiment shows that the median accuracy of this algorithm and the comparison algorithm are 0.97 and 0.92, respectively, with a distinction of 0.05, and the average values are 0.962 and 0.902, with a discrepancy of 0.060. About the accuracy, the improved algorithm is higher than that unimproved algorithm, with a difference of 16% and an average accuracy difference of 7.5%. Therefore, the constructed algorithm has effectiveness and feasibility, and to a certain extent has good development potential and reference value.

关键词： object detection clustering algorithm occlusion recognition attention mechanism separable convolution feature information

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DriverMEDS: Cancer driver gene identification using mutual exclusivity from embeded features and driver mutation scoring

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METHODS 2025年 239卷 22-29页

作者： Yi, Sichen Xie, Minzhu Hunan Normal Univ Sch Math & Stat Key Lab Comp & Stochast Math Minist Educ Changsha 410081 Peoples R China Hunan Normal Univ Coll Informat Sci & Engn Changsha 410081 Peoples R China

Efficiently identifying cancer driver genes plays a key role in the cancer development, diagnosis and treatment. Current unsupervised driver gene identification methods typically integrate multi-omics data into gene function networks and employ network embedding algorithms to learn gene features. Additionally, they consider mutual exclusivity and mutation frequency as crucial concepts in identifying driver genes. However, existing approaches neglect the possible important implications of mutual exclusivity in the embedding space. Furthermore, they simply assume that all driver genes exhibit high mutation frequencies. Fortunately, we explored the mutual exclusivity implanted in the learned features and have verified that the Euclidean distances between learned features are strongly related to the mutual exclusivity and they can reveal more information for the mutual exclusivity. Thus, we designed an unsupervised driver gene predicting framework DriverMEDS based on the above idea and a novel driver mutation scoring strategy. First, we design a feature clustering algorithm to generate gene modules. In each module, the Euclidean distances of learned features are used to calculate a module importance score for each gene based on the related mutual exclusivity. Then, following the fact that most of driver genes have intermediate mutation frequencies, a driver mutation scoring function is designed for each gene to optimize the existing mutation frequency scoring strategy. Finally, the weighted sum of the module importance score and the driver mutation score is used to prioritize the genes. The experiment results and analysis show that DriverMEDS could detect novel cancer driver genes and relevant function modules, and outperforms other five state-of-the-art methods for cancer driver identification.

关键词： Cancer driver Mutual exclusivity Embedded features clustering algorithm

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A Novel Density based clustering algorithm and Its Parallelization 15

A Novel Density based Clustering Algorithm and Its Paralleli...

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15th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

作者： Li, Xiaokang Yu, Binbin Zhou, Yinghua Sun, Guangzhong Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Anhui Peoples R China

ISBN: (纸本)9781479983346

k-Means, a simple but effective clustering algorithm, is widely used in data mining, machine learning and computer vision community. k-Means algorithm consists of initialization of duster centers and iteration. The initial duster centers have a great impact on duster result and algorithm efficiency. More appropriate initial centers of k-Means can get closer to the optimum solution, and even much quicker convergence. In this paper, we propose a novel clustering algorithm, Kmms, which is the abbreviation of k-Means and Mean Shift. It is a density based algorithm. Experiments show our algorithm not only costs less initialization time compared with other density based algorithms, but also achieves better clustering quality and higher efficiency. And compared with the popular k-Means++ algorithm, our method gets comparable accuracy, mostly even better. Furthermore, we parallelize Kmms algorithm based on OPenMP from both initialization and iteration step and prove the convergence of the algorithm.

关键词： Kmms clustering algorithm density parallelize

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A Greedy Dynamic clustering algorithm of Joint Transmission In Dense Small Cell Deployment

A Greedy Dynamic Clustering Algorithm of Joint Transmission ...

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IEEE 11th Consumer Communications and Networking Conference (CCNC)

作者： Qin, Cheng Tian, Hui Beijing Univ Posts & Telecommun State Key Lab Neworking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9781479923557

In a dense small cell deployment scenario, users are always prone to suffer severe interferences from neighbor base stations (BS) because the BSs are usually located closely. Coordinated Multi-Point (CoMP) can be introduced to alleviate these interferences and improve the system performance. It is necessary to determine coordination areas (CA) before implementation. In this paper, a novel dynamic clustering algorithm in CoMP joint transmission system is proposed based on graph theory. Firstly a feedback procedure is designed for interference reports mainly based on large scale fading. By building a graph according to the interferences, the clustering problem is equivalent to dividing the graph into several subgraphs. Each subgraph represents a CoMP cluster. It can be solved through a greedy strategy that each BS searches its best coordinated BSs. Compared with some other dynamic algorithms, the complexity of the proposed scheme is lower because it can be implemented in a decentralized way. Therefore this method is suitable in dense cell deployment with a large number of BSs. The simulation results show that the novel clustering algorithm performs better in user capacity than other traditional dynamic schemes. The influences of some parameters in this method are also considered and evaluated in the simulation.

关键词： Small cell deployment clustering algorithm Joint Transmission

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