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Commodity dynamic pricing and inventory joint decision-making model based on specific power demand function

Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia 2016年第4期39卷 74-81页

作者： Sun, Dejie Liu, Wenyuan Zhang, Jie School of Information Science and Engineering Yanshan University Qinhuangdao Hebei066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao Hebei066004 China

Dynamic pricing and inventory play a respective role. The purpose of inventory is to try best to maintain an even production speed and efficiently ease supply-and-demand contradiction during the production process and among enterprises in the supply chain. However, dynamic pricing aims at maximizing revenues by setting commodities or services at different price levels according to diversified demands of consumers and different commodity or service price evaluation by consumers in different periods. Therefore, it is important for enterprises to optimize their dynamic pricing strategies and commodity inventory so as to maximize value of researches. However, demands for tangible commodities are uncertain. Generally speaking, the commodity demand volume should first be confirmed before any specific analysis. Therefore, this paper focuses on introducing the commodity demand function and relevant factors influencing the commodity demand volume. The utility function describing consumers' demands for a single commodity is analyzed. The specific power demand function model based on utility maximization is built through the utility function. At last, we conduct an in-depth analysis and simulation of the model.

关键词： Economics

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Joint extraction method for entity relations from mineral resources reports integrating dependency parsing and improved graph convolutional networks

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Ore Geology Reviews 2025年 182卷

作者： Qiu, Qinjun Jin, Xiangguo Tian, Miao Wu, Qirui Tao, Liufeng Chen, Jianguo Xie, Zhong Technology Innovation Center for Geological Data Intelligent Application Department of Natural Resources of Jiangsu Province 210012 China School of Computer and Science China University of Geosciences Hubei Wuhan430074 China National Engineering Research Center for Geographic Information System Hubei Wuhan430074 China Key Laboratory of Geological Survery and Evaluation of Ministry of Education Hubei Wuhan430074 China School of Future Technology China University of Geosciences Hubei Wuhan430074 China Wuhan University Hubei Wuhan430074 China

Geological reports, as crucial technical documents reflecting the outcomes of geological survey work, encapsulate extensive expert and domain knowledge. Geological knowledge graphs integrate vast amounts of data, facilitating efficient and rapid extraction of knowledge embedded within geoscientific data. The extraction of geological entity relations is a key method in creating these knowledge graphs. Existing techniques for extracting geological entities and their relations encounter difficulties such entity overlap, relation overlap, and the challenge of obtaining deep semantic information because of the vastness and complexity of geological data. Our study suggests a collaborative extraction model for entity relations that integrates dependency syntactic relations with a graph convolutional network (GCN) in order to address these problems. This model learns dependency syntactic structures and deep semantic information by building a GCN that includes dependency syntactic relations. A pointer network decoder is then added to increase entity relation extraction efficiency. Dependencies between words in a phrase, such as subject-verb and verb-object relations, are revealed via dependency syntactic analysis. By structuring these dependencies into a graph, the model captures syntactic structural information. Through operations involving adjacency matrices and feature matrices, the model effectively propagates and aggregates node information, thereby capturing the global dependency syntactic structure and deep semantic information of sentences. The integration of dependency syntactic relations with GCN processing enables the model to more accurately comprehend entity relations within sentences. Results from experiments show that this model successfully tackles problems like overlapping entity relations and the challenge of gleaning deep semantic information from geological texts. It achieves a 79.73% accuracy rate and a 77.98% F1 score on geological text datasets. © 2025

关键词： Geochemical surveys

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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A real-time collision detection between virtual and real objects based on three-dimensional tracking of hand

A real-time collision detection between virtual and real obj...

引用

2010 International Conference on Audio, Language and Image Processing, ICALIP 2010

作者： Li, Yan Shen, Xukun State Key Laboratory of Virtual Reality Technology and System Beihang University China

ISBN: (纸本)9781424458653

In the working environment of argument reality, users interact with virtual objects by hands. Making real-time collision detection between virtual and real objects is very essential to enhance the immersion and authenticity of the environment. In this paper, the key points of hand are marked by color block. We use a pair of common web cameras to collect images and track the points. Then the stereo vision calibration algorithm is achieved .We get hand positioning by calculating the disparity of key points between two images and restoring the three-dimensional position. At the same time, the virtual object models are divided to a binary tree and pretreated by the OBB bounding box. At last, the accurate collision detection is achieved by calculating the relative position between real hand and virtual objects real-time. ©2010 IEEE.

关键词： Binary trees

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An Energy-Efficient Data Collection Scheme Using Denoising Autoencoder in Wireless Sensor Networks

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Tsinghua Science and technology 2019年第1期24卷 86-96页

作者： Guorui Li Sancheng Peng Cong Wang Jianwei Niu Ying Yuan School of Computer Science and Engineering Northeastern University School of Information Science and Technology Guangdong University of Foreign Studies State Key Laboratory of Virtual Reality Technology and Systems Beihang University

As one of the key operations in Wireless Sensor Networks(WSNs), the energy-efficient data collection schemes have been actively explored in the literature. However, the transform basis for sparsifing the sensed data is usually chosen empirically, and the transformed results are not always the sparsest. In this paper, we propose a Data Collection scheme based on Denoising Autoencoder(DCDA) to solve the above problem. In the data training phase, a Denoising AutoEncoder(DAE) is trained to compute the data measurement matrix and the data reconstruction matrix using the historical sensed data. Then, in the data collection phase, the sensed data of whole network are collected along a data collection tree. The data measurement matrix is utilized to compress the sensed data in each sensor node, and the data reconstruction matrix is utilized to reconstruct the original data in the ***, the data communication performance and data reconstruction performance of the proposed scheme are evaluated and compared with those of existing schemes using real-world sensed data. The experimental results show that compared to its counterparts, the proposed scheme results in a higher data compression rate, lower energy consumption, more accurate data reconstruction, and faster data reconstruction speed.

关键词： wireless sensor networks data collection neural networks autoencoder data reconstruction

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Adversarial image detection based on the maximum channel of saliency maps

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Optoelectronics Letters 2022年第5期18卷 307-312页

作者： FU Haoran WANG Chundong LIN Hao HAO Qingbo Key Laboratory of Computer Vision and System Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China

Studies have shown that deep neural networks(DNNs) are vulnerable to adversarial examples(AEs) that induce incorrect behaviors. To defend these AEs, various detection techniques have been developed. However, most of them only appear to be effective against specific AEs and cannot generalize well to different AEs. We propose a new detection method against AEs based on the maximum channel of saliency maps(MCSM). The proposed method can alter the structure of adversarial perturbations and preserve the statistical properties of images at the same time. We conduct a complete evaluation on AEs generated by 6 prominent adversarial attacks on the Image Net large scale visual recognition challenge(ILSVRC) 2012 validation sets. The experimental results show that our method performs well on detecting various AEs.

关键词： networks neural maps

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Coalition Formation for Heterogeneous Federated Learning Enabled Channel Estimation in RIS-assisted Cell-free MIMO

arXiv

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arXiv 2025年

作者： Qi, Nan Liu, Haoxuan Tsiftsis, Theodoros A. Boulogeorgos, Alexandros-Apostolos A. Zhou, Fuhui Jin, Shi Wu, Qihui Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Informatics and Telecommunications University of Thessaly Lamia35100 Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani50100 Greece National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China

Downlink channel estimation remains a significant bottleneck in reconfigurable intelligent surface-assisted cell-free multiple-input multiple-output communication systems. Conventional approaches primarily rely on centralized deep learning methods to estimate the high-dimensional and complex cascaded channels. These methods require data aggregation from all users for centralized model training, leading to excessive communication overhead and significant data privacy concerns. Additionally, the large size of local learning models imposes heavy computational demands on end users, necessitating strong computational capabilities that most commercial devices lack. To address the aforementioned challenges, a coalition-formation-guided heterogeneous federated learning (FL) framework is proposed. This framework leverages coalition formation to guide the formation of heterogeneous FL user groups for efficient channel estimation. Specifically, by utilizing a distributed deep reinforcement learning (DRL) approach, each FL user intelligently and independently decides whether to join or leave a coalition, aiming at improving channel estimation accuracy, while reducing local model size and computational costs for end users. Moreover, to accelerate the DRL-FL convergence process and reduce computational burdens on end users, a transfer learning method is introduced. This method incorporates both received reference signal power and distance similarity metrics, by considering that nodes with similar distances to the base station and comparable received signal power have a strong likelihood of experiencing similar channel fading. Massive experiments performed that reveal that, compared with the benchmarks, the proposed framework significantly reduces the computational overhead of end users by 16%, improves data privacy, and improves channel estimation accuracy by 20%. © 2025, CC BY.

关键词： Federated learning

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PRAD: Periapical Radiograph Analysis Dataset and Benchmark Model Development

arXiv

引用

arXiv 2025年

作者： Zhou, Zhenhuan Zhang, Yuchen Xu, Ruihong Zhao, Xuansen Li, Tao College of Computer Science Nankai University Tianjin300350 China Key Laboratory of Data and Intelligent System Security Ministry of Education China Department of stomatology Tianjin Union Medical Center The First Affiliated Hospital of Nankai University Tianjin300121 China College of Mechanical Engineering Taiyuan University of Technology Shanxi 030000 China Haihe Lab of ITAI Tianjin300459 China

Deep learning (DL), a pivotal technology in artificial intelligence, has recently gained substantial traction in the domain of dental auxiliary diagnosis. However, its application has predominantly been confined to imaging modalities such as panoramic radiographs and Cone Beam Computed Tomography, with limited focus on auxiliary analysis specifically targeting Periapical Radiographs (PR). PR are the most extensively utilized imaging modality in endodontics and periodontics due to their capability to capture detailed local lesions at a low cost. Nevertheless, challenges such as resolution limitations and artifacts complicate the annotation and recognition of PR, leading to a scarcity of publicly available, large-scale, high-quality PR analysis datasets. This scarcity has somewhat impeded the advancement of DL applications in PR analysis. In this paper, we present PRAD-10K, a dataset for PR analysis. PRAD-10K comprises 10,000 clinical periapical radiograph images, with pixel-level annotations provided by professional dentists for nine distinct anatomical structures, lesions, and artificial restorations or medical devices, We also include classification labels for images with typical conditions or lesions. Furthermore, we introduce a DL network named PRNet to establish benchmarks for PR segmentation tasks. Experimental results demonstrate that PRNet surpasses previous state-of-the-art medical image segmentation models on the PRAD-10K dataset. The codes and dataset will be made publicly available. © 2025, CC BY-NC-ND.

关键词： Dental orthoses

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GPU accelerating for rapid multi-core cache simulation

GPU accelerating for rapid multi-core cache simulation

引用

25th IEEE International Parallel and Distributed Processing Symposium, Workshops and Phd Forum, IPDPSW 2011

作者： Han, Wan Xiang, Long Gao, Xiaopeng Yi, Li State Key Laboratory of Virtual Reality Technology and System School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9780769543857

To find the best memory system for emerging workloads, traces are obtained during application's execution, then caches with different configurations are simulated using these traces. Since program traces can be several gigabytes, simulation of cache performance is a time consuming process. Compute unified device architecture (CUDA) is a software development platform which enables programmers to accelerate the general-purpose applications on the graphics processing unit (GPU). This paper presents a real time multi-core cache simulator, which was built based on the Pin tool to get the memory reference;and fast method for multi-core cache simulation using the CUDA-enabled GPU. The proposed method is accelerated by the following techniques: execution parallelism exploration;memory latency hiding;a novel trace compression methodology. We describe how these techniques can be incorporated into CUDA code. Experimental results show that the hybrid parallel method of time-partitioning combines with set-partitioning presented here is 11.10× speedup compared to the CPU serial simulation algorithm. The present simulator can characterize cache performance of single-threaded or multi-threaded workloads at the speeds of 6-15 MIPS. It can simulates 6 cache configurations within one single pass at this speeds compared to CMP$im, which can only simulate one cache configuration each simulation pass at the speeds of 4-10 MIPS. © 2011 IEEE.

关键词： Graphics processing unit

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Efficient subtree results computation for XML keyword queries

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Frontiers of computer Science 2015年第2期9卷 253-264页

作者： Ziyang CHEN School of Information Science and Engineering Yanshan University Hebei 066004 China Environmental Management College of China Hebei 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province Yanshan University Hebei 066004 China

In this paper, we focus on efficient construction of restricted subtree （RSubtree） results for XML keyword queries on a multicore system. We firstly show that the perfor- mance bottlenecks for existing methods lie in 1） computing the set of relevant keyword nodes （RKNs） for each subtree root node, 2） constructing the corresponding RSubtree, and 3） parallel execution. We then propose a two-step generic top-down subtree construction algorithm, which computes SLCA/ELCA nodes in the first step, and parallelly gets RKNs and generates RSubtree results in the second step, where generic means that 1） our method can be used to compute dif- ferent kinds of subtree results, 2） our method is independent of the query semantics; top-down means that our method con- structs each RSubtree by visiting nodes of the subtree con- structed based on an RKN set level-by-level from left to right, such that to avoid visiting as many useless nodes as possible. The experimental results show that our method is much more efficient than existing ones according to various metrics.

关键词： XML SLCA/ELCA RSubtree RKNs parallel

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