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21st Chinese National Conference on Computational Linguistic, CCL 2022

作者： Fangyuan, Zhu Zhiqiang, Ma Zhiqiang, Liu Caijilahu, Bao Hongbin, Wang Inner Mongolia University of Technology Huhhot010000 China Inner Mongolia Autonomous Region Software Service Engineering Technology Research Center Based on Big Data Huhhot010000 China

The speaker features extracted by the speaker feature extraction model have low discrimination, which makes the mongolian acoustic model unable to learn the discrimination information, resulting in the model unable to adapt to different speakers. A speaker adaptation method based on attention is proposed. The method introduces neural Turing machine for adaptation, adds a memory module to store the speaker features, uses the attention mechanism to calculate the similarity weight matrix between the speaker features in the memory module and the current voice speaker features, and recombines the weight matrix into the speaker features s-vector, so as to improve the discrimination between the speaker features. On the IMUT-MCT dataset, the ablation experiment, model adaptation experiment and case analysis of speaker feature extraction method are carried out. The experimental results show that comparing s-vector, i-vector and d-vector with different speaker characteristics, s-vector reduces SER and WER by 4.96% and 1.08% respective-ly compared with the other two methods;By comparing different mongolian acoustic models, the performance of the proposed method is improved compared with the baseline. © 2022 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License.

关键词： Speech recognition

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Distributed Data Center Cooling control based on Multi-Agent Reinforcement Learning

Distributed Data Center Cooling control based on Multi-Agent...

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Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Ding Chen Jianxiong Wan Leixiao Li Chuyi Liu Inner Mongolia University of Technology Hohhot China nner Mongolia Autonomous Region Engineering \& Technology Research Center of Big Data Based Software Service Hohhot China

The data center cooling system is a complex-structured system integrated with a large number of components. Traditional centralized control methods are hard to apply in production data centers due to scalability issues. This paper studies the distributed cooling control problem where the data center is cooled by multiple Computer Room Air Conditioners (CRACs) to minimize the energy consumption. To characterize the interactions of CRACs, we formulate the distributed data center cooling control problem as a stochastic game, where each CRAC acts as an agent to learn the optimal local cooling policy. We propose a Multi-Agent Reinforcement Learning (MARL) framework based on the Counterfactual Multi-Agent (COMA). Specifically, we policies based on the agents' observations of the global state. After the implementation of these policies, they will receive their own rewards, then evaluate the quality of the policies. Simulation results show that compared with the Independent Q-Learning (IQL) method, this algorithm reduces the power consumption of CRACs by 6.2%.

关键词： Data centers Q-learning Cooling Simulation Scalability software algorithms Stochastic processes

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Model Predictive Control for Carbon-Neutral Data Centers

Model Predictive Control for Carbon-Neutral Data Centers

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IEEE International Conference on Systems, Man and Cybernetics

作者： Guanyu Ren Jianxiong Wan Leixiao Li Chuyi Liu Xiaolei Wang Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China China

As one of the major carbon producers, data centers produce around 1% of global carbon emissions per year. Researchers are making significant effort to reduce the data center carbon emissions. However, the current carbon-neutral data center solutions seldom take the carbon quotas into account, nor do they integrate new emission reduction technologies like Carbon Capture (CC) and Power-to-Gas (P2G), etc. To bridge this gap, in this paper a novel carbon-neutral data center architecture is proposed based on which a holistic cost minimizing problem is formulated. We use the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) neural network to construct highly accurate prediction models, and develop a Model Predictive Control (MPC) algorithm for energy and carbon management. Finally, simulations on real-world data demonstrate that our approach reduces up to 19.19% overall cost compared with traditional solutions.

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A Simple Dashboard for OpenStack Horizon 2021

A Simple Dashboard for OpenStack Horizon

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2021 4th International Conference on Computing and Big Data

作者： Hongbin Wang Shaoxu Li Dan Deng Leixiao Li Jing Gao Jie Li College of Data Science and Application Inner Mongolia University of Technology China and Inner Mongolia Autonomous Region Software Service Engineering Technology Research Center based on Big Data China College of Computer and Information Engineering Inner Mongolia Agricultural University China

ISBN: (纸本)9781450387194

OpenStack is an open source cloud computing management project, which allows flexible resources applying. The traditional OpenStack dashboard is inflexible, thus a simple dashboard is designed. The simple dashboard integrates keystone, horizon, grace, nova, neutron, swift and other components, communicates with the traditional OpenStack dashboard and runs in the form of docker container. We implemented simple dashboard on a small cloud platform. Compared with the traditional dashboard, the interface of simple dashboard is simpler, more flexible and easy to use.

关键词： Cloud computing Horizon OpenStack Simple dashboard

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Performance Evaluation and Machine Learning based Thermal Modeling of Tilted Active Tiles in Data Centers 2020

Performance Evaluation and Machine Learning based Thermal Mo...

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5th International Conference on Machine Learning Technologies, ICMLT 2020

作者： Rasheed, Zeeshan Xiong, Wei Cong, Gaoxiang Niu, Hongxun Wan, Jianxiong Wang, Yongsheng Li, Lixiao Inner Mongolia University of Technology and Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service China

ISBN: (纸本)9781450377645

Thermal management system of data center continuously face a lot of challenges, because data center industry has seen a boom growth in power density. In this paper we proposed the Tilted Active Tiles (TATs) to improve the local cold air supply and prevent the air flow blow over the rack. In traditional active tiles, fans are placed horizontally which cause the airflow blows over the rack, rather than into, the racks. To solve this issue, we adjusted the angle of the active tile to direct the airflow into the rack. We further introduced ANN based thermal models to predict the thermal performance of TATs. To train the ANN models, we adopted the data set obtained from a data center of inner mongolia Meteorology Information Center. The prediction accuracy of the model was extensively compared and analyzed, and the prediction accuracy and overhead of different neural network structures, i.e., BP and LSTM, were evaluated. Experimental results show that the rack with blanking panels has better thermal performance, and the temperature distribution at bottom, middle and top of the rack were same under smaller PWM. Thermal efficiency model was established by BP and LSTM, in this experiment single output model and multi output model were analyzed. The single output model can predict the temperature at different heights on the rack. In single output model the predicted effect of BP model is better than LSTM. The average prediction error is 0.57. The multi-output model can only predict the temperature at a fixed height of the rack. In multi output model LSTM model is better than BP. LSTM prediction error is less than BP. The average prediction error is 0.07. © 2020 ACM.

关键词： Thermoanalysis

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Reinforcement Learning for Rack-Level Cooling 9th

Reinforcement Learning for Rack-Level Cooling

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9th International Conference on Mobile Wireless Middleware, Operating Systems and Applications, MOBILWARE 2020

作者： Duan, Yanduo Wan, Jianxiong Zhou, Jie Cong, Gaoxiang Rasheed, Zeeshan Hua, Tianyang Inner Mongolia University of Technology Inner Mongolia China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot China

ISBN: (纸本)9783030622046

In recent years, we have seen a rapid growth in big data and cloud computing industry, because of the rapid development in Internet technology. That’s why the number and scale of data center have increased rapidly. A data center is a warehouse-level IT facility that hosts many servers. Because of the uneven heat production and heat dissipation of the servers in a rack, the hot spots emerges. In order to maintain the CPU temperature hence the computing performance, the server cooling is very critical. A common solution is to increase the speed of the Computer Room Air Handler (CRAH) blower and increase the flow of cold air. Nevertheless, this solution can only partially address the issue and raise the cooling energy consumption. In this paper, we study how to mitigate rack hot spots without significantly increasing the power of air conditioning system. We propose the Active Ventilation Tiles (AVTs), i.e., ordinary ventilation tiles with attached fans, to enhance the local cold air delivery and improve the cooling performance. In particular, we propose an AVT control algorithm adapted from the Reinforcement Learning techniques to tackle the complex data center environment and thermo dynamic process. The reinforcement learning algorithm adjusts the temperature distribution of the rack by controlling the fan speed installed on the ventilation tile, and guides the fan speed according to the feedback temperature to mitigate hot spots. Due to the slow learning speed of the traditional Tabular-Q-Learning algorithm, we integrate the Tabular-Q-Learning algorithm with the Dyna architecture to accelerate the learning speed and improve the algorithm performance in the early stage. Experimental results reveal that Tabular-Q-learning based on Dyna has better performance. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Reinforcement learning

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A Parameter Transfer Method for HMM-DNN Heterogeneous Model with the Scarce mongolian Data Set

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Procedia Computer Science 2021年 187卷 258-263页

作者： Zhiqiang Ma Junpeng Zhang Tuya Li Rui Yang Hongbin Wang Inner Mongolia University of Technology Hohhot 010080 China Inner Mongolia Autonomous Region Engineering & Technology Research Centre of Big Data Based Software Service Hohhot 010080 China

Hidden Markov Model-Deep Neural Network (HMM-DNN) is one of the most successful architecture in speech recognition. Although the HMM-DNN achieved state-of-the-art results on English and Mandarin, we find that there are lots of not updated parameters during the training of HMM-DNN acoustic model on a small scale mongolian data set. This caused the model’s network training underfitting, which cannot learn the features of the data set. In the speech recognition scenario, the underfitting of speech features leads to a problem that the accuracy of the system decreases. In this regard, we define the concept of the homogeneous model heterogeneous model and propose a parameter learning method for HMM-DNN heterogeneous model in the scarce mongolian data set. In our experiment, we choose KALDI as the experimental platform with the TIMIT English data set as the source data set and the scarce mongolian data set as the target data set. Through the proposed parameter transfer method, we achieved much better performance on mongolian recognition accuracy.

关键词： HMM-DNN Model Homogeneous Model Heterogeneous Model Parameter Transfer Transfer Learning

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Optimal Sharding for Dynamic Throughput Optimization in Blockchain Systems with Deep Reinforcement Learning

Optimal Sharding for Dynamic Throughput Optimization in Bloc...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Bingbing Yao Jianxiong Wan Shan Jaffry Leixiao Li Zhiqiang Ma Chuyi Liu Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China China Syntronic Research and Development Canada Inc.

The rapid advancement in blockchain technology has enabled its applications across wide spectrum of fields. The blockchain throughput, which is usually measured by Transactions Per Second (TPS), is one of the key metrics to reflect the performance of the blockchain systems. However, current blockchain systems have low TPS rates that makes them unsuitable for latency critical applications like Vehicle-to-vehicle (V2V) communication. To address the above issue, the sharding technology, which divides the network into multiple disjoint groups so that transactions can be processed in parallel, is applied to the blockchain systems as a promising solution to improve TPS. This paper considers the Optimal Blockchain Sharding (OBCS) problem which is formulated as a Markov Decision Process (MDP) where the decision variables are the number of shards, block size and block interval. Previous works solved the OBCS problem via Deep Reinforcement Learning (DRL) based methods where the action space has to be discretized such that it is not too large for tractability. However, the discretization degrades the solution quality since the optimal solution usually lies between discrete values. In this paper, we treat the block size and block interval as continuous decision variables and propose a sharding control algorithm based on Parametrized Deep Q-Networks (P-DQN) to efficiently handle the discrete-continuous hybrid action space without the scalability issue. Experimental results show that our Parametrized Deep Q-Networks Blockchain Sharding (P-DQNBS) method can effectively improve the TPS by up to 20%.

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Ultra-Short-Term Wind Power Prediction Model with Financial Factors Based on the Lightgbm Algorithm

SSRN

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SSRN 2022年

作者： Wang, Yong Sheng Guan, Shi Jie Liu, Li Min Jing, Gao Xu, Zhi Wei Liu, Guang Wen School of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Software Service Engineering Technology Research Center Inner Mongolia Autonomous Region Hohhot010080 China School of Computer and Information Inner Mongolia Agricultural University Hohhot010018 China

This study introduces an ultra-short-term wind power prediction model with financial factors based on the LightGBM algorithm, which innovatively introduces the KDJ and MACD from financial time series data into the wind power time series data to address the challenges of existing prediction models due to the uncertainty factors such as wind speed and wind direction. The experiments show that the model has satisfactory prediction results and a short prediction time, which can contribute to developing grid dispatching plans and adjusting national strategies. © 2022, The Authors. All rights reserved.

关键词： Wind power

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Modeling thunderstorm based on paralleled and improved naive bayes 2

Modeling thunderstorm based on paralleled and improved naive...

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2020 2nd International Conference on Computer Modeling, Simulation and Algorithm, CMSA 2020

作者： Zhou, Chengdong Li, Leixiao Wang, Hui Liu, Shuang College of Data science and Application Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Region Engineering and Technology Research Center ofBig Data Based Software Service Hohhot China

Aiming at improving the low efficiency and inaccuracy of the traditional thunderstorm prediction model, a paralleled and improved naive Bayesian thunderstorm prediction model SPNBC is proposed. The native Bayesian classifier (NBC) model assumes that the attribute data set is independent, which leads to inaccurate prediction. The principal component analysis algorithm is used to optimize the Bayesian algorithm to build PNBC. First, a new attribute data set is constructed by the principal component analysis (PCA) algorithm to eliminate the dependency between attributes, and a naive Bayesian classifier is constructed on the new attribute data set. Secondly, SPNBC is obtained by parallel design of PNBC based on spark framework to improve the time efficiency. Finally, taking the thunderstorm prediction in Hohhot as the application background, the accuracy rate, false alarm rate, speedup ratio and scalability ratio were used to analyze the experiment. The experimental results show that the SPNBC of this paper is better than the traditional naive Bayes algorithm and neural network algorithm, and the prediction accuracy rate and empty alarm rate are larger when dealing with massive data Performance advantages. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： Thunderstorms

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