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检索条件"机构=Control and Computer Engineering"

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On the Complexity of Computing the Maximal Positive Invariant Set

On the Complexity of Computing the Maximal Positive Invarian...

引用

European control Conference (ECC)

作者： Bogdan Gheorghe Florin Stoican Ionela Prodan Faculty of Automation Control and Computer Science National University of Science and Technology Politehnica Bucharest Romania Grenoble INP LCIS Univ. Grenoble Alpes Institute of Engineering and Management Univ. Grenoble Alpes Valence France

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Computing the maximal (robust) positive invari-ant (M(R)PI) set for linear dynamics and a polyhedral constraint set is well-known in the literature but, the effects and limitations of the different methods employed are not sufficiently clear, especially for high dimensional systems. In this paper we propose a systematic analysis of the existing techniques as well as the application of new ideas to accelerate the computation of the MPI set. This includes new stop conditions for the set recurrence that spans it. We analyze and compare these variations over a dynamical system whose dimension can be arbitrarily increased to draw conclusions about their relative strengths and weaknesses.

关键词： Systematics Europe Complexity theory Dynamical systems Predictive control

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Security enhancement of LSB-based audio steganography method

Security enhancement of LSB-based audio steganography method

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Jovana Jezdimirović Nenad Pekez Jelena Kovačević Computing and Control Engineering Department Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia RT-RK Institute for Computer Based Systems LLC Novi Sad Serbia

The rapid development of digital technology has brought about the challenge of ensuring information security. Cryptography and steganography are among the various techniques available to address this challenge. These techniques come in different forms and provide reliable means of securing information and communication. Despite the existence of numerous options, new variations are still emerging. This paper concentrates on Least Significant Bit (LSB) coding, which is one of the most widely recognized and frequently utilized steganography techniques. The advantages of LSB-based coding methods are high capacity and low complexity. However, predictability is an issue. Audio files’ sizes and data redundancy make audio data perfect for steganography, as it is possible to embed large amounts of secret information and easily transmit the signal via various communication channels. However, the use of LSB coding in audio steganography has a drawback due to the extreme sensitivity of the human auditory system (HAS). This means that any noise added to the audio with data embedded in the LSBs can be detected by the HAS, which is dependent on the number of LSBs used. This work presents an improvement of the LSB-based approach, the XORing of LSBs method, with an emphasis on enhanced security. The method achieves this by randomly picking the pairs of bits to be XOR’d while preserving the characteristics of classic LSB methods. To verify the method, it has been implemented on the Cirrus Logic DSP, proving that it may be efficiently used in a real-time audio environment without interfering with HAS sensitivity levels.

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Reliability Analysis of Concurrent Data based on Botnet Modeling 4

Reliability Analysis of Concurrent Data based on Botnet Mode...

引用

4th International Conference on Inventive Systems and control, ICISC 2020

作者： Cui, Lianhe Huang, Dehai Zheng, Xiaodong Qiqihar University College of Computer and Control Engineering Heilongjiang Qiqihar161006 China

ISBN: (数字)9781728128139

ISBN: (纸本)9781728128139

Reliability analysis of concurrent data based on Botnet modeling is conducted in this paper. At present, the detection methods for botnets are mainly focused on two aspects. The first type requires the monitoring of high-privilege systems, which will bring certain security risks to the terminal. The second type is to identify botnets by identifying spam or spam, which is not targeted. By introducing multi-dimensional permutation entropy, the impact of permutation entropy on the permutation entropy is calculated based on the data communicated between zombies, describing the complexity of the network traffic time series, and the clustering variance method can effectively solve the difficulty of the detection. This paper is organized based on the data complex structure analysis. The experimental results show acceptable performance. © 2020 IEEE.

关键词： Botnet

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PV powered neuromorphic sensors with optical connectivity

PV powered neuromorphic sensors with optical connectivity

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Communications and Networking (BalkanCom), International Balkan Conference on

作者： George-Iulian Uleru Alexandru Barleanu Mircea Hulea Zabih Ghassemlooy Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iasi Iasi Romania Photonics Technology Laboratory Optical Communications Research Group. FEE Northumbria University Newcastle upon Tyne United Kingdom

ISBN: (数字)9798350365955

ISBN: (纸本)9798350365962

The spiking neural networks (SNN) benefits from low power consumption, very good signal-to-noise ratio and the ability to model rigorously the physiology of the biological neural areas. In robotics, the SNN can be used in different applications including motion control where the neural module drives the actuators based on the information from sensors. The most suitable type of sensing devices in biomimetic robotics are the neuromorphic sensors (NS) with spiking output which can include an optical transmitter for wireless connectivity. Considering that the reduced energy consumption is a critical characteristic for the SNN, in this work we evaluate the possibility of using photovoltaic (PV) panels to power the NS with optical output. The focus is on the recently introduced type of NS with integrated force sensing resistor (FSR) that uses a module based on a light emitting diode (LED) to generate optical pulses. We measured the responses of this NS with the load mass when it is powered by a PV panel, and the results show that the NS operates in nominal conditions despite the slight variations of the used supply voltage. Moreover, the NS is able to transmit optical pulses which frequency depends on the load mass.

关键词： Biomedical optical imaging Neuromorphics Robot sensing systems Optical network units Optical receivers Sensors Optical sensors

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A Multi-Discriminator Weighted Adversarial Network for Cross-Domain Unknown Wind Turbine Fault Diagnosis

A Multi-Discriminator Weighted Adversarial Network for Cross...

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IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)

作者： Yinghua Han Huaiyuan Qi Fangyuan Si Kangping Li Qiang Zhao School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Control Engineering Notheastern University at Qinhuangdao Qinhuangdao China Department of Electrical Engineering Tsinghua University Beijing China The College of Smart Energy Shanghai Jiao Tong University Shanghai China

The practical cross-domain fault diagnosis methods in wind turbines based on transfer learning have been widely studied, where most of the existing transfer learning methods assume that test data and training data have the same label space. However, this assumption is not realistic in practical wind turbines because the data in the target domain wind turbine often presents new fault categories during the testing phase, which can affect the accuracy of the model. A multi-discriminator weighted adversarial network (MWAN) is proposed here to handle the above problem. The proposed method can automatically identify outliers in the source domain and unknown fault classes in the target domain by introducing a hybrid weighting balanced strategy and using an auxiliary discriminator to weigh source and target domain samples. Adversarial training is introduced to achieve shared class-level alignment in the two domains. Experiments verify that the proposed method outperforms state-of-the-art methods for wind turbine fault diagnosis applications.

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Emotion Recognition in Social Network Texts Based on A Multilingual Architecture

Emotion Recognition in Social Network Texts Based on A Multi...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Jiajun Zou Yexuan Zhang Jinshuai Yang Sixing Wu Minghu Jiang Yongfeng Huang School of Humanities Tsinghua University Beijing China Department of Electronic Engineering Tsinghua University Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China Department of Electronic Engineering Zhongguancun Laboratory Tsinghua University Beijing China

Languages are used by people to describe and categorize their emotional experiences and perspectives. For many applications, it is crucial to apply techniques like machine learning in social network texts to identify emotions. Most of these technologies now in use only detect a small number of emotion categories such as anger, happiness, sadness and so on, they do not distinguish more fine-grained levels of emotions. Additionally, they frequently concentrate on modeling the relationships between various emotions, ignoring the emotional semantic relations between different languages. Therefore, in this paper, we improve the Recognition of Emotion by utilizing a Multilingual architecture that combines machine Translation and Attention mechanism, enabling one language to provide additional emotional information for another language (REMTA). The experimental results on a fine-grained emotion dataset labeled with 28 categories show a performance improvement compared with other models, demonstrating the efficacy of our architecture.

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Machine Learning-Based Underwater Communication System Using Light Carrying Orbital Angular Momentum

Machine Learning-Based Underwater Communication System Using...

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Conference on Lasers and Electro-Optics (CLEO)

作者： William A. Jarrett Svetlana Avramov-Zamurovic Joel M. Esposito Charles Nelson Weapons Robotics and Control Engineering Department United States Naval Academy Electrical and Computer Engineering Department United States Naval Academy Annapolis Maryland USA

An underwater communication system using light carrying orbital angular momentum is evaluated using a convolutional neural network through simulated and experimental thermally-generated underwater optical turbulence. ... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Underwater communication Optical computing Optical fiber networks Lasers and electrooptics Orbits Convolutional neural networks Electrooptical waveguides

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Research on the Evaluation of Predictive Power Data Service Based on Improved Entropy Weight and Cloud Model

Research on the Evaluation of Predictive Power Data Service ...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Chunhui Zhao Danli Li Jiyong Zhang Gao Yu Jiangyu Yan Hong Wang YuLian Bo State Grid Economic and Technological Research Institute Co. Ltd. Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

Predictive power data services are an effective way to unleash the value of power data. In response to the current lack of effective evaluation for such services, this paper proposes an evaluation system and method for assessing the quality of predictive power data services. The objective is to enhance service quality, improve service benefits, and provide better decision support and management tools for the power industry. The proposed approach is based on the cloud model principle. The golden section method is employed to generate the cloud, while the improved entropy weight method is used to calculate the weights. The effectiveness of the proposed evaluation method is validated through the evaluation of predictive power data services provided in different regions.

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Short Circuit and Connection Fault Diagnosis for Lithium-ion Battery Pack Based on Differential Voltage Changes and Independent Component Analysis

Short Circuit and Connection Fault Diagnosis for Lithium-ion...

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Energy, Power and Electrical engineering (EPEE), International Conference on

作者： Luan Zhang Hanxiao Liu Yang Li Liwei Li School of Control Science and Engineering Shandong University Jinan China Qingdao Power Supply Company Shandong Electric Power Company State Grid Qingdao China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

In order to avoid thermal runaway as well as ensure safe and stable operation of battery system, it is vital to perform rapid and accurate diagnosis for early faults with similar characteristics. In this paper, a short circuit and connection fault diagnosis method that is applicable to traditional one-to-one voltage sensor topology is proposed based on differential voltage changes (DVCs) and independent component analysis. Voltage measurements is pretreated to obtain DVCs for extracting fault signature and reducing the detrimental effects of inconsistency, interference, etc. to a certain extent. Independent component analysis is combined with the DVC vector to establish diagnostic model and diagnose faults online. Fault detection and isolation is achieved using squared prediction error, and considering that traditional contributions cannot accurately determine fault location, an improved contribution calculation is introduced to localize the faulty cell. Experimental verifications show that the proposed method can timely detect and localize faults, meanwhile it can distinguish short circuit with connection fault.

关键词： Lithium-ion batteries Voltage measurement Accuracy Independent component analysis Vectors Real-time systems Topology Circuit faults Thermal stability Load modeling

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Comparative Techno-Economic-Environmental Analysis of HVDC and HVAC Technologies for an Offshore Wind Farm in China

Comparative Techno-Economic-Environmental Analysis of HVDC a...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Yingqi Liang Shuangxi Wu Yang Liu Dongqiang Fu Yan Rui Lee Gengrui Guo Tianhao Luo Tianhao Wang Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd. Guangzhou China

DC collection systems offer advantages by reducing the weight and size of DC cables without requiring reactive power compensation. This enables the replacement of the bulky 50/60 Hz transformers typically used in AC collection systems on offshore platforms with smaller medium-frequency transformers in DC collection configurations. Nonetheless, challenges persist in implementing high-power DC-DC converters with high-voltage transformation ratios and DC protection methods in DC collection systems. It is worth noting that while HVDC (High Voltage DC) transmission can transfer offshore wind power from collection systems to onshore grids, DC collection systems do not always result in fewer power conversion stages compared to AC collection systems. To tackle these challenges, this paper conducts a comparative analysis of the technological, economic, and environmental aspects of DC and AC collection systems for offshore wind farms, using a wind farm in China as an illustrative example. Our approach involves an innovative method for estimating losses and a technical comparison. Simulation results validate that DC collection systems exhibit higher total losses than AC collection systems. We also explore the impact of collection bus voltages on these losses in DC systems. Additionally, we develop an economic cost assessment method, and the sensitivity analysis results confirm that cost reductions primarily stem from the reduced size of DC cables and offshore platforms rather than improvements in DC protective devices and DC-DC converters. Lastly, we investigate the environmental implications of these systems.

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