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Establishment and Application of Steel Composition Prediction Model Based on t-distributed Stochastic Neighbor Embedding (t-SNE) Dimensionality Reduction Algorithm

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JOURNAL OF SUSTAINABLE METALLURGY 2024年第2期10卷 509-524页

作者： Liu, Xin Bao, Yanping Zhao, Lihua Gu, Chao Univ Sci & Technol Beijing State Key Lab Adv Met Beijing 100083 Peoples R China Univ Sci & Technol Beijing Met & Ecol Engn Sch Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Innovat Sch Foshan 528399 Guangdong Peoples R China

To meet the goals of the national "Dual Carbon" strategy and reduce energy consumption in the steel industry, accurate prediction of steel composition is crucial for precise control over alloy addition in steelmaking. Several models have been created to predict the composition of the converter endpoint with a high level of accuracy. However, the different shortcomings of each have prevented large-scale application in real production environments. CBR prediction model has limited scope to solve the problem. CNN model has complex data processing and no memory. RELM model has randomly given input layer weights and hidden layer deviations. In this study, correlation analysis was used to analyze the factors influencing the carbon content at the endpoint of converter steelmaking. A feasible model was established and applied to predict the carbon content at the endpoint of converter using t-distributed stochastic neighbor embedding (t-SNE), particle swarm optimization (PSO), and backpropagation (BP) neural network. The learning rate, training times, and hidden layer nodes number of the prediction model were optimized. The prediction hit ratios for the carbon content in the error ranges of +/- 0.003%, +/- 0.01%, and +/- 0.02% are 61%, 86%, and 98%, respectively. Meanwhile, apply the established model to actual production, the carbon content of the product can be stably controlled between the lower and median limits, the control effect is significantly better than traditional methods. The results demonstrate that the t-SNE-PSO-BP model performs better than the known models. The accurate prediction of the carbon content at the endpoint of converter can greatly contribute to realizing a "narrow composition control" of the molten steel. Realize accurate prediction of carbon content at the endpoint of converter smelting, and has been effectively applied to industrial *** AbstractUnder the traditional method of predicting the endpoint carbon content of the conver

关键词： Converter endpoints Carbon content t-distributed stochastic neighbor embedding Prediction model Industrial application

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Recognizing the mapping relationship between wind power output and meteorological information at a province level by coupling GIS and CNN technologies

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APPLIED ENERGY 2024年 360卷

作者： Zhang, Juntao Cheng, Chuntian Yu, Shen Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China Dalian Univ Technol Key Lab Ocean Energy Utilizat & Energy Conservat Minist Educ Dalian 116023 Peoples R China

Estimating the total wind power output from the meteorological information at a province level (called Provincial Regional Wind Power Conversion Model, PRWPCM) plays vital and fundamental roles in energy modeling community and regional wind power forecasting. How to construct a reliable PRWPCM is a real challenge, since PRWPCM involves a large number of widely distributed wind turbines, massive meteorological data across the whole province, and complex nonlinear correlations. This paper proposes a lightweight PRWPCM by integrating Geographic Information System (GIS) analysis technology and Convolutional neural network (CNN). First, we conduct the land suitability analysis for wind turbine sites through the multi-criteria GIS layer overlay method to make the provincial wind turbine land suitability map (WTLSM) with scored divisions from the least suitable to the most suitable areas. On this basis, a new fusion mechanism for geographic and meteorological information is proposed, through which the raw meteorological data matrix can be reconstructed to filter and amplify the meteorological information that is more relevant to the total wind power output of the province, and avoid the time-consuming and labor-intensive data collection and processing, large-size model construction and validation. Second, a CNN-based regression architecture is designed to further capture the mapping relationship between the reconstructed meteorological data and total wind power output of the province;each type of meteorological factor is considered as an input channel and the attention modules are introduced to adaptively enhance useful channels and suppress less useful ones. Numerical experiments based on the wind power operation data of Yunnan Province, China, are conducted to validate the superiority of the proposed PRWPCM via benchmarking against 13 classical methods.

关键词： Wind power output Meteorological information Geographic information system Convolutional neural network

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distributed Inference and Fine-tuning of Large Language Models Over The Internet 37

Distributed Inference and Fine-tuning of Large Language Mode...

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37th Conference on neural Information processing Systems (NeurIPS)

作者： Borzunov, Alexander Ryabinin, Max Chumachenko, Artem Baranchuk, Dmitry Dettmers, Tim Belkada, Younes Samygin, Pavel Raffel, Colin HSE Univ Moscow Russia Yandex Moscow Russia Neiro Ai San Francisco CA USA Univ Washington Seattle WA USA Hugging Face New York NY USA Yandex Sch Data Anal Moscow Russia

ISBN: (纸本)9781713899921

Large language models (LLMs) are useful in many NLP tasks and become more capable with size, with the best open-source models having over 50 billion parameters. However, using these 50B+ models requires high-end hardware, making them inaccessible to most researchers. In this work, we investigate methods for cost-efficient inference and fine-tuning of LLMs, comparing local and distributed strategies. We observe that a large enough model (50B+) can run efficiently even on geodistributed devices in a consumer-grade network. This could allow running LLM efficiently by pooling together idle compute resources of multiple research groups and volunteers. We address two open problems: (1) how to perform inference and fine-tuning reliably if any device can disconnect abruptly and (2) how to partition LLMs between devices with uneven hardware, joining and leaving at will. In order to do that, we develop special fault-tolerant inference algorithms and load-balancing protocols that automatically assign devices to maximize the total system throughput. We showcase these algorithms in PETALS1 - a decentralized system that runs Llama 2 (70B) and BLOOM (176B) over the Internet up to 10x faster than offloading for interactive generation. We evaluate the performance of our system in simulated conditions and a real-world setup spanning two continents.

关键词： Inference engines

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A Two-phase Encrypted Traffic Classification Scheme in Programmable Data Plane 22

A Two-phase Encrypted Traffic Classification Scheme in Progr...

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22nd IEEE International Symposium on Parallel and distributed processing with Applications, ISPA 2024

作者： Wang, Hao Tan, Xiaobin Yuan, Shenzhi Li, Mengxiang Wu, Jiansong Zheng, Quan University of Science and Technology of China Hefei China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China

ISBN: (纸本)9798331509712

The importance of encrypted traffic classification for network management and security is self-evident. The emergence of programmable data plane (PDP) technology makes it possible to directly implement encrypted traffic classification in the data plane, which can classify network traffics in line-rate. In this paper, we propose a two-phase encrypted traffic classification (TP-ETC) scheme in programmable data plane. In TP-ETC, Convolutional neural network (CNN) is employed for classifying highly similar traffic with high accuracy in the first phase, and Long Short-Term Memory (LSTM) model is responsible for classifying all remaining traffic with low storage overhead in the second phase, achieving the best balance between accuracy and storage overhead. We also design a feature extraction method suitable for PDP, effectively reducing the overhead of feature storage. In addition, we design a table segmentation algorithm to reduce the growth rate of table entries to a linear level. The experimental results demonstrate the superiority of the proposed scheme TP-ETC. © 2024 IEEE.

关键词： Convolutional neural networks

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Spatiotemporal Information processing in Living Neuronal network in vitro

Spatiotemporal Information Processing in Living Neuronal Net...

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作者： Ibne Ferdous, Zubayer Lehigh University

学位级别：博士

Our brain can differentiate many objects and time-dependent changes in the object features. This functionality requires information processing in both spatial and temporal domains. Studies showed that cortical microcircuits are intrinsically responsive to spatiotemporal patterns. Confined neural networks in vitro obtained from the brain cortex with an optical interface were used to classify spatiotemporal information in this work. This experimental setup was designed by confining a cortical network in a polydimethylsiloxane (PDMS) well and by co-transfecting the neurons with channel-rhodopsin 2 (ChR2) and jRgeco1a. In line with other studies, our data showed that neurons in dissociated cortical cultures are very susceptible to synchronized bursts, particularly in the confined network due to synaptic scaling. As these bursts represent chaotic activation and do not carry information about the input, we first designed a stimulation protocol to suppress bursts with distributed patterns of optical activation in the network. We found that increases in the Ca2+ baseline level of all neurons were correlated with successful burst suppression and the success rate was higher when the culture medium was replaced by artificial cerebrospinal fluid (ACSF) with high [Mg2+]. In the absence of population bursts, our results showed that the state of the living network could be used to classify input spatial and temporal patterns. Our experimental model shows a paradigm of reservoir computing with a living neural network that could run learning applications with small sets of training data and computational resources. This model can further be used to study toxicity or influences of commercial drugs on spatiotemporal information in neural networks.

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Change of motifs in C. elegans reveals developmental principle of neural network

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BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 2022年 624卷 112-119页

作者： Liu, Jian Yuan, Ye Zhao, Peng Liu, Gaofeng Huo, Hong Li, Zhaoyu Fang, Tao Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Univ Queensland Queensland Brain Inst Brisbane Qld 4072 Australia

Revealing the organizing principles of developing neural networks is a difficult but significant task in neuroscience. As a creature with a rather compact and well-studied neural network, C. elegans is an ideal subject for neuroscience study. However, the researches on its developing neural network remain challenging. The changes in specific properties of neural network across development may uncover part of its principles. Motif is a typical structure property that can be well applied to various complex networks. Here, we study the motif changes in C. elegans neural network across development. By counting the occurrence number of all three-node subgraph motif structures in its neural network at different stages of C. elegans development, along with those in corresponding random networks, we determine which of these structures are motifs for C. elegans, finding out the regular changes of motifs during its development. Combined with the potential function of these subgraph motifs and synaptic information, we gain insight into the organizing principle of neural network during development, which may increase our understanding of neuroscience and inspire the construction of artificial neural network. (c) 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Motifs Organizing principle neural network Development C. elegans

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Voltage Sags Characterization Using Fault Analysis and Deep Convolutional neural networks

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第3期58卷 3333-3341页

作者： Turizo, Sergio Ramos, Gustavo Celeita, David Univ Andes Dept Elect & Elect Engn Bogota 111711 Colombia Univ Rosario Sch Engn Sci & Technol Bogota 111711 Colombia

The power systems are transitioning to renewables. The power system infrastructure digitalization is the next stage in this evolution. A digitalized future distribution network (DN) has an opportunity to evolve into real-time analysis based on a huge volume of data. Such real-time analysis will be feasible through the integration of the theoretical background of fault analysis and machine learning techniques. Fault location is one of the major issues to improve reliability indices. The voltage sags characterization is used to locate faults in the DN. This article presents a methodology to characterize voltage sags using fault analysis and deep convolutional neural networks. The voltage divider model allows the characterization and the discrete wavelet transform is used in signal processing. The machine learning and deep learning models used allow estimating the sag magnitude, fault location, phases involved, duration, and impact of distributed generation (DG) in each event. The IEEE 13-node test feeder including DG was used to validate the effectiveness of the proposed methodology. This paper provides a way to handle a Big Data stream in the DN control center and to efficiently locate faults in several operational scenarios.

关键词： Circuit faults Power quality Power systems Fault location Load modeling Impedance neural networks Convolutional neural networks (CNNs) distributed generation (DG) fault analysis unbalanced faults voltage sags

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Fractional-order bi-Hopfield neuron coupled via a multistable memristor: Complex neuronal dynamic analysis and implementation with microcontroller

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2025年 191卷

作者： Tamba, Victor Kamdoum Biamou, Arsene Loic Mbanda Pham, Viet-Thanh Grassi, Giuseppe Tagne, Francois Kapche Takougang, Armand Cyrille Nzeukou Univ Dschang Dept Telecommun & Network Engn IUT Fotso Victor Bandjoun POB 134 Bandjoun Cameroon Univ Dschang Dept Elect Engn Res Unit Automat & Appl Comp IUT FV Bandjoun POB 134 Bandjoun Cameroon Ind Univ Ho Chi Minh City Fac Elect Technol Ho Chi Minh City Vietnam Univ Salento Dept Engn Innovat I-73100 Lecce Italy Univ Dschang Fac Sci Dept Phys Res Unit Condensed Matter Elect & Signal Proc POB 67 Dschang Cameroon

The Hopfield neuron is an artificial neuron model used for pattern memorization and recognition. It exhibits a complex dynamic with stable states corresponding to memorized patterns. In order to grasp a more complete representation of the information exchange between two neurons, emphasizing the importance of neuronal connections in brain processing, we propose in this work the coupling of two fractional-order Hopfield neurons via a fractional-order flux-controlled multistable memristor. Each of these two neurons incorporates a selfcoupling memristive component, called an autapse memristor. Additionally, the second neuron is subjected to an external electromagnetic radiation, simulated by an additional memristor. The I-V characteristics of the memristors integrated in this model are analyzed through numerical simulations. The simulations of model dynamics versus its diverse parameters have revealed rich and complex dynamical behaviors. These simulations demonstrate that the proposed model generates a variety of homogeneous and heterogeneous chaotic attractors, distributed at diverse locations. The elaborated memristor coupled fractional-order bi-Hopfield neuron MCFBHN model is implemented on an Arduino-Due platform. A comparison of the results of the two approaches shows good consistency.

关键词： Fractional-order memristive Hopfield neural network Autapse memristor External electromagnetic radiation memristor Memristor initial boosting behaviors Microcontroller implementation

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Enabling distributed Inference of Large neural networks on Resource Constrained Edge Devices using Ad Hoc networks 1

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20th International Symposium on distributed Computing and Artificial Intelligence, DCAI 2023

作者： Ohlenforst, Torsten Schreiber, Moritz Kreyß, Felix Schrauth, Manuel Am Wolfsmantel 33 Erlangen91058 Germany

ISBN: (数字)9783031383335

ISBN: (纸本)9783031383328

processing neural network inferences on edge devices, such as smartphones, IoT devices and smart sensors can provide substantial advantages compared to traditional cloud-based computation. These include technical aspects such as low latency or high data throughput – but also data sovereignty can be a concern in many applications. Even though general approaches of distributed inference have been developed recently, a transfer of these principles to the edge of the network is still missing. In an extreme edge setting, computations typically are severely constraint by available energy resources and communication limitations among individual devices, which makes the distribution and execution of large-scale neural networks particularly challenging. Moreover, since mobile devices are volatile, existing static networks are unsuited and instead a highly dynamic network architecture needs to be orchestrated and managed. We present a novel, multi-stage concept which tackles all associated tasks in one framework. Specifically, distributed inference approaches are complemented with necessary resource management and network orchestration in order to enable distributed inference in the field – hence paving the way towards a broad number of possible applications, such as autonomous driving, traffic optimization, medical applications, agriculture, and industry 4.0. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy resources

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Road network Representation Learning with the Third Law of Geography 38

Road Network Representation Learning with the Third Law of G...

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38th Conference on neural Information processing Systems, NeurIPS 2024

作者： Zhou, Haicang Huang, Weiming Chen, Yile He, Tiantian Cong, Gao Ong, Yew-Soon College of Computing and Data Science Nanyang Technological University Singapore Institute of High-Performance Computing Agency for Science Technology and Research Singapore Centre for Frontier Al Research Agency for Science Technology and Research Singapore

Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we identify the limitations of existing methods, particularly their overemphasis on the distance effect as outlined in the First Law of Geography. In response, we propose to endow road network representation with the principles of the recent Third Law of Geography. To this end, we propose a novel graph contrastive learning framework that employs geographic configuration-aware graph augmentation and spectral negative sampling, ensuring that road segments with similar geographic configurations yield similar representations, and vice versa, aligning with the principles stated in the Third Law. The framework further fuses the Third Law with the First Law through a dual contrastive learning objective to effectively balance the implications of both laws. We evaluate our framework on two real-world datasets across three downstream tasks. The results show that the integration of the Third Law significantly improves the performance of road segment representations in downstream tasks. Our code is available at https://***/Haicang/Garner. © 2024 neural information processing systems foundation. All rights reserved.

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