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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Li, Yingquan Liang, Zhenwen N'Doye, Ibrahima Zhang, Xiangliang Alouini, Mohamed-Slim Laleg-Kirati, Taous-Meriem KAUST Comp Elect & Math Sci & Engn Div CEMSE Thuwal 239556900 Saudi Arabia Univ Notre Dame Notre Dame IN 46556 USA INRIA Paris France

ISBN: (纸本)9781713872344

Light-Emitting Diodes (LEDs) based underwater optical wireless communications (UOWCs), a technology with low latency and high data rates, have attracted significant importance for underwater robots. However, maintaining a controlled line of sight link between transmitter and receiver is challenging due to the constant movement of the underlying optical platform caused by the dynamic uncertainties of the LED model and vibration effects. Additionally, the alignment angle required for tracking is not directly measured and has to be estimated. Besides, the light scattering propagates beam pulse in water temporally, resulting in nonlinearities and time-varying underwater optical links with interference and introducing challenges in the estimation problem. In this paper, we address the state estimation problem by designing a Luenberger observer for the LED communication system that provides the angular position and velocity state information involved in the challenges of maintaining a controlled LOS optical wireless communication. In this line, we leverage the power of deep learning-based observer design to estimate the state of the LED communication model online. Simulation results are presented to illustrate the performance of the data-driven LED state estimation. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC- ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Light-emitting diode underwater optical wireless communication online estimation observer design nonlinear systems deep learning algorithm neural networks.

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Detection of Fake Tweets Using Sentiment Analysis

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SN Computer Science 2020年第2期1卷 1-7页

作者： Monica, C. Nagarathna, N. Department of Computer Science B. M. S. College of Engineering Bengaluru 560019 India

Social platform is one of the most commonly used sites in the today’s world, and people from different places exchange information, express opinions, etc. Twitter is one of the most convenient platforms where Twitter data are frequently used for research and are one of the largest micro-blogging platforms. As there is an increase in number of spam accounts, there is a need to distinguish between genuine and fake user accounts. In Twitter, there is rise in the number of spam accounts, to spread malicious information among the users. So, to overcome the problem of spam in Twitter, an analysis of the opinions and the emotions of users in the form of tweets have been carried out. In this paper, we present a model which performs sentimental analysis on real-time data that are collected on a particular topic of interest for the users who recently posted on that topic. Using this model, we compute the sentiment score of each user with the content-based features to detect spam in Twitter. The proposed method makes use of a custom rule-based algorithm for detection of bots and compares it with few other algorithms like MLP, decision tree and random forest to see how efficient the model is in detection of spam accounts. © 2020, Springer Nature Singapore Pte Ltd.

关键词： deep learning algorithm Machine learning algorithm Rule-based algorithm Sentiment analysis Spam Twitter

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An active model for ranging by deep convolutional neural network and elephant herding optimization algorithm (DCNN-EHOA) in WSNs

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INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS 2022年第2期18卷 236-249页

作者： Reddy, Adireddy Rajasekhar Rao, Appini Narayana NBKR Inst Sci & Technol Nellore India

Purpose In modern technology, the wireless sensor networks (WSNs) are generally most promising solutions for better reliability, object tracking, remote monitoring and more, which is directly related to the sensor nodes. Received signal strength indication (RSSI) is main challenges in sensor networks, which is fully depends on distance measurement. The learning algorithm based traditional models are involved in error correction, distance measurement and improve the accuracy of effectiveness. But, most of the existing models are not able to protect the user's data from the unknown or malicious data during the signal transmission. The simulation outcomes indicate that proposed methodology may reach more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods. Design/methodology/approach This paper present a deep convolutional neural network (DCNN) from the adaptation of machine learning to identify the problems on deep ranging sensor networks and overthrow the problems of unknown sensor nodes localization in WSN networks by using instance parameters of elephant herding optimization (EHO) technique and which is used to optimize the localization problem. Findings In this proposed method, the signal propagation properties can be extracted automatically because of this image data and RSSI data values. Rest of this manuscript shows that the ECO can find the better performance analysis of distance estimation accuracy, localized nodes and its transmission range than those traditional algorithms. ECO has been proposed as one of the main tools to promote a transformation from unsustainable development to one of sustainable development. It will reduce the material intensity of goods and services. Originality/value The proposed technique is compared to existing systems to show the proposed method efficiency. The simulation results indicate that this proposed methodology can achieve more constant and accurate positio

关键词： Wireless sensor networks Convolutional neural network EHO algorithm deep learning algorithm Signal propagation

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Design and Development of the AI-assisted Safety System for Hazardous Plant 13

Design and Development of the AI-assisted Safety System for ...

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13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Fang, Laihua Liang, Jinxiong Jiang, Liu Wang, Enguo China Safety Technol Dev Co Ltd China Acad Safety Sci & Technol Beijing Peoples R China China Safety Technol Dev Co Ltd China Merchants Grp Shenzhen Peoples R China

ISBN: (纸本)9780738105451

In order to promote the safety degree of hazardous chemicals storage plant, an intelligent safety system based on Artificial Intelligence (AI), Internet of Things (IOT) and other information technologies, is proposed. Structure of the safety system is presented;main functions and core techniques of the system are designed. deep learning algorithm assisted supervision technology to provide warnings for the "unsafe state of things, unsafe behavior of people and environmental factors" is investigated in details. Application of the intelligent safety system designed shows good results, which help to improve the operation efficiency of safety management obviously, and reduce the accidents risk effectively.

关键词： hazardous chemicals plant safety system monitor artificial intelligence deep learning algorithm camera

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Preliminary Study on the Method of Infrared Image Reconstruction Based on SRGAN Network 20th

Preliminary Study on the Method of Infrared Image Reconstruc...

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20th Chinese Intelligent Systems Conference

作者： Sun, Baiping Tong, Jiahui Geng, Huapin Beijing Electromech Engn Inst Beijing Peoples R China Sci & Technol Complex Syst Control & Intelligent Beijing Peoples R China

ISBN: (纸本)9789819786602;9789819786589;9789819786572

With the continuous improvement of the resolution of detectors and experimental equipments, the original low-resolution real samples can not meet the training requirements of simulation and recognition algorithm. Simply Stretching the image can cause the blur and Mosaic of the image. The image areas and blocks cannot truly represent the optical characteristics. During the enhancement process, the details are destroyed. Therefore, more efficient and accurate methods should be explored for infrared scene generation, so as to improve the authenticity and diversity of infrared imaging simulation. This paper mainly aims at the above problems, introduces super-resolution generated adversarial network, and draws the advantages of its jumping connection and deep residual network structure, defining new loss function to improve the output image. Exploring ways to improve the quality of infrared images and solve the problems of limited number of samples and low resolution in the process of infrared imaging simulation.

关键词： Infrared imaging guidance simulation deep learning algorithm SRGAN

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Using Speech Acts to Elicit Positive Emotions for Complainants on Social Media

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JOURNAL OF INTERACTIVE MARKETING 2021年第1期55卷 67-80页

作者： Argyris, Young Anna Monu, Kafui Kim, Yongsuk Zhou, Yilu Wang, Zuhui Yin, Zhaozheng Michigan State Univ Coll Commun Arts & Sci Dept Media & Informat 404 Wilson Rd E Lansing MI 48824 USA Univ Northern British Columbia Sch Business Management Informat Syst Area Prince George BC Canada Sungkyunkwan Univ Sch Business Management Informat Syst Area Seoul South Korea Fordham Univ Gabelli Sch Business Informat Syst Area New York NY 10023 USA SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA SUNY Stony Brook Artificial Intelligence Inst Dept Biomed Informat Dept Comp SciDept Appl Math & Stat Stony Brook NY 11794 USA

A carefully tailored tone in response to a complaint on social media can create positive emotions for an upset customer. However, very few studies have identified what response tones, based on an established theory, would be most effective for complaint management. This study conceptualizes a service agent's response tones based on Ballmer and Brennenstuhl's (1981) classification of speech acts and examines how an agent's use of speech acts elicit positive emotions for the complainant. Ballmer and Brennenstuhl classify speech acts within the dimensions of conventionality and dialogicality, and they suggest the two dimensions interact. Thus, we examine the impact of each dimension of speech acts and the interactions between the two dimensions on the elicitation of positive emotions for complainants. We collected over 100,000 tweets and classified firm agents' speech acts and complainants' emotions by designing deep learning architectures (i.e., bi-directional recurrent neural networks). Our fixed-effect regression results show that a low level of each speech act leads to the elicitation of customers' positive emotions but that the combination of the two erodes the individual advantages. This study expands Ballmer and Brennenstuhl's (1981) speech act classification from a speaker's perspectives to a listener's perspectives by contextualizing it in an analysis of service agents' tones and their roles in eliciting positive emotions among complainants. (c) 2021 Direct Marketing Educational Foundation, Inc. dba Marketing EDGE. All rights reserved.

关键词： Complaint management Social media deep learning algorithm Speech act theory

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Modeling lithium-ion Battery in Grid Energy Storage Systems: A Big Data and Artificial Intelligence Approach 6

Modeling lithium-ion Battery in Grid Energy Storage Systems:...

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IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)

作者： Miao, Yong He, Xinyuan Gu, Chenghong Shanghai Elect Power Transmiss & Distribut Grp Shanghai Peoples R China Univ Bath Dept Elect & Elect Engn Bath BA2 7AY England

ISBN: (纸本)9798350311259

Grid energy storage system (GESS) has been widely used in smart homes and grids, but its safety problem has impacted its application. Battery is one of the key components that affect the performance of GESS. Its performance and working conditions directly affect the safety and reliability of the power grid. With the development of data analytics and machine learning, the accuracy and adaptability of the battery state estimation model can be greatly improved. This paper proposes a new method to model battery, with low-quality data. First, it designs a data cleaning method for GESS battery operating data, including missing data filling and outlier data repair. Then, the repaired data is used to model battery. A battery mathematical model is proposed based on a deep learning algorithm to realize accurate GESS state estimation. The performance of the developed deep learning method is compared with conventional BP neural network and generalized regression neural network to highlight the technical merits. Results derived in this paper provide a solid basis for high-efficiency GESS operation and energy management.

关键词： Grid energy storage system Big data Battery modeling Battery state estimation deep learning algorithm Artificial intelligence

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Guillaume Broux-Quemerais,Sarah Kaakai, Anis Matoussi,Wissal Sabbagh

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Probability, Uncertainty and Quantitative Risk 2024年第2期9卷 149-180页

作者： Guillaume Broux-Quemerais Sarah Kaakarl Anis Matoussi Wissal Sabbagh Laboratoire Manceau de Mathematiques&FR CNRS No 2962 Institut du Risque et de I'AssuranceLeMansUniversityFrance Centre de Mathématiques Appliquees(CMAP) CNRSEcole polytechniqueInstitut Polytechnique de Paris91120 PalaiseauFrance

In this paper,we present a probabilistic numerical method for a class of forward utilities in a stochastic factor *** this purpose,we use the representation of forward utilities using the ergodic Backward Stochastic Differential Equations(eBSDEs)introduced by Liang and Zariphopoulou in[27].We establish a connection between the solution of the ergodic BSDE and the solution of an associated BSDE with random terminal time T,defined as the hitting time of the positive recurrent stochastic *** viewpoint based on BSDEs with random horizon yields a new characterization of the ergodic cost^which is a part of the solution of the *** particular,for a certain class of eBSDEs with quadratic generator,the Cole-Hopf transformation leads to a semi-explicit representation of the solution as well as a new expression of the ergodic cost>.The latter can be estimated with Monte Carlo *** also propose two new deep learning numerical schemes for ***,we present numerical results for different examples of eBSDEs and forward utilities together with the associated investment strategies.

关键词： deep leaming scheme Forward utilities Ergodic BSDEs Markovian solution deep learning algorithm

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An Efficient Parallel Framework to Analyze Astronomical Sky Survey Data 1

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1st International Conference on Big Scientific Data Management (BigSDM)

作者： Ma, Xiaobin Du, Zhihui Sun, Yankui Tchernykh, Andrei Wu, Chao Wei, Jianyan Tsinghua Univ Dept Comp Sci & Technol Tsinghua Natl Lab Informat Sci & Technol Beijing Peoples R China CICESE Res Ctr Ensenada Baja California Mexico Chinese Acad Sci Natl Astron Observ Beijing Peoples R China

ISBN: (数字)9783030280611

ISBN: (纸本)9783030280611;9783030280604

Big data has been an important analysis method anywhere we turn today. We hold broad recognition of the value of data, and products obtained through analyzing it. There are multiple steps to the data analysis pipeline, which can be abstracted as a framework provides universal parallel high-performance data analysis. Based on ray, this paper proposed a parallel framework written in Python with an interface to aggregate and analyze homogeneous astronomical sky survey time series data. As such, we can achieve parallel training and analysis only by defining the customized analyze functions, decision module and I/O interfaces, while the framework is able to manage the pipeline such as data fetching, saving, parallel job scheduling and load balancing. Meanwhile, the data scientists can focus on the analysis procedure and save the time speeding this program up. We tested out the framework on synthetic data with raw files and HBase entries as data sources and result formats, reduced the analyze cost for scientists not familiar with parallel programming while needs to handle a mass of data. We integrate time series anomaly detection algorithms with our parallel dispatching module to achieve high-performance data processing frameworks. Experimental results on synthetic astronomical sky survey time series data show that our model achieves good speed up ratio in executing analysis programs.

关键词： Big data analysis deep learning algorithm Performance optimization

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Real-Time Behavioral Biometric Information Security System for Assessment Fraud Detection

Real-Time Behavioral Biometric Information Security System f...

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IEEE International Conference on Computing (ICOCO)

作者： Subash, Aditya Song, Insu James Cook Univ Coll Sci & Engn Singapore Singapore

ISBN: (纸本)9781665436892

Online education has become a major delivery method in education. Many schools have adopted online delivery of courses. This has exposed schools to greater information security risks, such as assessment cheating, identity theft, and loss of sensitive student information. To counter emerging new types of attacks, risks, and vulnerabilities, the protection measures must be able to adapt to the evolving behaviors of both user and attacker by learning new emerging vulnerabilities and behaviors of users. We propose a new Real-time Behavioral Biometric Information Security (RBBIS) architecture that non-invasively builds behavioral profiles on the fly using deep-learning approaches. The method learns behaviors of students to validate users and detect intrusion, identity theft, and assessment fraud. This greatly improves the current limitations of the existing user authentication approaches of online education platforms. RBBIS was evaluated using CNN deep-learning keystroke behavior biometric analysis and compared with various existing machine learning algorithms: j48, Naive Bayes, and Multi Layered Perceptron (MLP). The results show that our deep-learning method performed best with a Convolutional Neural Network (CNN) with 92.45% of accuracy, whereas Naive Bayes, j48 and MLP achieved accuracies of 68.87%, 73.38% and 77.11%, respectively.

关键词： Assessment fraud online education platforms sensitive student information keystroke analysis user validation and authentication real time behavioral biometric data deep learning algorithm

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