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Zhen Pan

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6 papers

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EAAI Journal 2022 Journal Article

Mechanical equipment health management method based on improved intuitionistic fuzzy entropy and case reasoning technology

Yupeng Gao
Ruixin Bao
Zhen Pan
Guiyang Ma
Jia Li
Xiuquan Cai
Qiqiang Peng

Management becomes more challenging as machinery becomes more widely used. From the health management history case records of mechanical devices, we can find that there are many health problems of the same type occurring repeatedly, but when similar problems occur again, solutions are not found in a short period. Case-based reasoning technology aimed at solving the above problems are widely used in equipment health management, but there are problems such as inadequate data utilization and failure to achieve the required accuracy and reliability. To uncover important information from historical failure case data and help reduce equipment downtime due to failure, this paper proposes a machinery equipment health management method based on improved intuitionistic fuzzy entropy and case reasoning technology. Firstly, the axiomatic definition of traditional intuitionistic fuzzy entropy is optimized and a new intuitionistic fuzzy entropy formula in the framework of case-based reasoning decision matrix is proposed. Secondly, the weight value of each attribute is obtained by combining the formula of the entropy weight method. Finally, the feature attribute weight values are fused into the case-based reasoning, and the historical cases that are most similar to the target cases are obtained by combining the existing case base. The effectiveness of the method was verified by comparing and analyzing the example calculation results with the traditional method. The method improves the management of machinery equipment operation and maintenance data. Furthermore, it helps enterprises to carry out the management and analysis of machinery equipment health problems.

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AAAI Conference 2020 Conference Paper

A Variational Point Process Model for Social Event Sequences

Zhen Pan
Zhenya Huang
Defu Lian
Enhong Chen

Many events occur in real-world and social networks. Events are related to the past and there are patterns in the evolution of event sequences. Understanding the patterns can help us better predict the type and arriving time of the next event. In the literature, both feature-based approaches and generative approaches are utilized to model the event sequence. Feature-based approaches extract a variety of features, and train a regression or classiﬁcation model to make a prediction. Yet, their performance is dependent on the experience-based feature exaction. Generative approaches usually assume the evolution of events follow a stochastic point process (e. g. , Poisson process or its complexer variants). However, the true distribution of events is never known and the performance depends on the design of stochastic process in practice. To solve the above challenges, in this paper, we present a novel probabilistic generative model for event sequences. The model is termed Variational Event Point Process (VEPP). Our model introduces variational auto-encoder to event sequence modeling that can better use the latent information and capture the distribution over inter-arrival time and types of event sequences. Experiments on real-world datasets prove effectiveness of our proposed model.

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AAAI Conference 2020 Conference Paper

Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach

Yang Liu
Qi Liu
Hongke Zhao
Zhen Pan
Chuanren Liu

In recent years, considerable efforts have been devoted to developing AI techniques for ﬁnance research and applications. For instance, AI techniques (e. g. , machine learning) can help traders in quantitative trading (QT) by automating two tasks: market condition recognition and trading strategies execution. However, existing methods in QT face challenges such as representing noisy high-frequent ﬁnancial data and ﬁnding the balance between exploration and exploitation of the trading agent with AI techniques. To address the challenges, we propose an adaptive trading model, namely iRDPG, to automatically develop QT strategies by an intelligent trading agent. Our model is enhanced by deep reinforcement learning (DRL) and imitation learning techniques. Speciﬁcally, considering the noisy ﬁnancial data, we formulate the QT process as a Partially Observable Markov Decision Process (POMDP). Also, we introduce imitation learning to leverage classical trading strategies useful to balance between exploration and exploitation. For better simulation, we train our trading agent in the real ﬁnancial market using minute-frequent data. Experimental results demonstrate that our model can extract robust market features and be adaptive in different markets.

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AAAI Conference 2020 Conference Paper

Crowdfunding Dynamics Tracking: A Reinforcement Learning Approach

Jun Wang
Hefu Zhang
Qi Liu
Zhen Pan
Hanqing Tao

Recent years have witnessed the increasing interests in research of crowdfunding mechanism. In this area, dynamics tracking is a signiﬁcant issue but is still under exploration. Existing studies either ﬁt the ﬂuctuations of timeseries or employ regularization terms to constrain learned tendencies. However, few of them take into account the inherent decision-making process between investors and crowdfunding dynamics. To address the problem, in this paper, we propose a Trajectory-based Continuous Control for Crowdfunding (TC3) algorithm to predict the funding progress in crowdfunding. Speciﬁcally, actor-critic frameworks are employed to model the relationship between investors and campaigns, where all of the investors are viewed as an agent that could interact with the environment derived from the real dynamics of campaigns. Then, to further explore the in-depth implications of patterns (i. e. , typical characters) in funding series, we propose to subdivide them into fast-growing and slow-growing ones. Moreover, for the purpose of switching from different kinds of patterns, the actor component of TC3 is extended with a structure of options, which comes to the TC3-Options. Finally, extensive experiments on the Indiegogo dataset not only demonstrate the effectiveness of our methods, but also validate our assumption that the entire pattern learned by TC3-Options is indeed the U-shaped one.

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AAAI Conference 2020 Conference Paper

Estimating Early Fundraising Performance of Innovations via Graph-Based Market Environment Model

Likang Wu
Zhi Li
Hongke Zhao
Zhen Pan
Qi Liu
Enhong Chen

Well begun is half done. In the crowdfunding market, the early fundraising performance of the project is a concerned issue for both creators and platforms. However, estimating the early fundraising performance before the project published is very challenging and still under-explored. To that end, in this paper, we present a focused study on this important problem in a market modeling view. Speciﬁcally, we propose a Graphbased Market Environment model (GME) for estimating the early fundraising performance of the target project by exploiting the market environment. In addition, we discriminatively model the market competition and market evolution by designing two graph-based neural network architectures and incorporating them into the joint optimization stage. Finally, we conduct extensive experiments on the real-world crowdfunding data collected from Indiegogo. com. The experimental results clearly demonstrate the effectiveness of our proposed model for modeling and estimating the early fundraising performance of the target project.

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EAAI Journal 2020 Journal Article

Short-term natural gas consumption prediction based on Volterra adaptive filter and improved whale optimization algorithm

Weibiao Qiao
Zhe Yang
Zhangyang Kang
Zhen Pan

Short-term natural gas consumption prediction is an important indicator of natural gas pipeline network planning and design, which is of great significance. The purpose of this study is to propose a novel hybrid forecast model in view of the Volterra adaptive filter and an improved whale optimization algorithm to predict the short-term natural gas consumption. Firstly, Gauss smoothing and C–C method is adopted to pretreat and reconstruct short-term natural gas consumption time series; secondly, to improve the performance of whale optimization algorithm, adaptive search-surround mechanism and spiral position and jumping behavior are introduced into it; Thirdly, Volterra adaptive filter is used to predict the short-term natural gas consumption, and the important parameters (e. g. embedding dimension) is optimized by improved whale optimization algorithm. Finally, an actual example is given to test the performance of the developed prediction model. The results indicate that (1) short-term natural gas consumption time series has chaotic characteristics; (2) performance of the improved whale optimization algorithm is better than some comparative algorithms (i. e. cuckoo optimization algorithm, etc. ) based on the different evaluation indicators; (3) exploration factor is the main operational factor; (4) the performance of the proposed prediction model is better than some advanced prediction models (e. g. back propagation neural network). It can be concluded that such an innovative hybrid prediction model may provide a reference for natural gas companies to achieve intelligent scheduling.

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