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6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022

作者： Purwanti, Hani Rochim, Adian Fatchur Warsito, Budi Diponegoro University Master of Information System Semarang Indonesia Diponegoro University Department of Computer Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Statistics Semarang Indonesia

ISBN: (数字)9781665497428

ISBN: (纸本)9781665497428

The increasing use of social media is challenging the old concept of customer relationship management (CRM). Social CRM strategy is a new model of CRM that applies social technology by presenting a new way to manage customer relationships. Compared to traditional CRM, social media is an affordable tool for SMEs to compete in the global market. Although the application of social CRM can increase business in SMEs, the implementation of social CRM still requires various conditions, especially for SMEs with limited resources. So for the adoption of social CRM, it is necessary to determine the factors that influence the adoption of social CRM in SMEs. This study aims to predict the adoption of social CRM by proposing a TOEP application model and developing several hypotheses that test the function of technology factors, Organizational factors, Environment factors, and Information Process factors. The proposed hypothesis model was tested using the SEM method on data taken from SMEs in the Banyumas area with a sample of 115 SMEs. This study finds that Relative Advantage, Complexity, Compatibility CRMS, Employee IT/SI Knowledge, Government Support, Information Use, Information Retrieval are the most important factors influencing the adoption of social CRM. This study differs from previous studies because it proposes a new model, namely the TOE adoption model and the information process factor as an additional factor to determine the factors that affect social CRM in SMEs. © 2022 IEEE.

关键词： Scanning electron microscopy

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Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking Control

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1262-1272页

作者： Mingming Ha Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Information Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA IEEE

The core task of tracking control is to make the controlled plant track a desired *** traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps *** this paper,a new cost function is introduced to develop the value-iteration-based adaptive critic framework to solve the tracking control *** the regulator problem,the iterative value function of tracking control problem cannot be regarded as a Lyapunov function.A novel stability analysis method is developed to guarantee that the tracking error converges to *** discounted iterative scheme under the new cost function for the special case of linear systems is ***,the tracking performance of the present scheme is demonstrated by numerical results and compared with those of the traditional approaches.

关键词： Adaptive critic design adaptive dynamic programming(ADP) approximate dynamic programming discrete-time nonlinear systems reinforcement learning stability analysis tracking control value iteration(VI)

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A New Five-Level Grid-Connected PV Inverter Topology Controlled By Model Predictive 48

A New Five-Level Grid-Connected PV Inverter Topology Control...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Sarebanzadeh, Maryam Hosseinzadeh, Mohammad Ali Garcia, Cristian Babaei, Ebrahim Jolfaei, Alireza Rodriguez, Jose Kennel, Ralph Technical University of Munich Chair of High-Power Converters System Munich Germany Universidad de Talca Faculty of Engineering Talca Chile University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran Flinders University College of Science and Engineering AdelaideSA Australia Universidad San Sebastian Faculty of Engineering Santiago Chile

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

The transformer-based inverters in PV systems increase the weight, size, and cost of the inverter while reducing efficiency. This research presents a new PV inverter topology to increase efficiency using a reduction of dc-link. The proposed multilevel inverter is comprised of six power switches, one discrete diode, and three capacitors to produce five voltage levels. The proposed inverter is connected to a PV panel at input and a local grid at output to inject a sinusoidal current waveform into the grid. To control the grid current, a finite set model predictive control is needed to evaluate the proposed inverter. A comparison study is carried out between the proposal and other five-level inverters to verify the strengths and weaknesses of the proposed multilevel inverter. Finally, to demonstrate the performance of the proposed multilevel inverter, the simulation results are presented in the MATLAB/Simulink environment. © 2022 IEEE.

关键词： Model predictive control

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PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain 23

PipeEdge: A Trusted Pipelining Collaborative Edge Training b...

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2023 World Wide Web Conference, WWW 2023

作者： Yuan, Liang He, Qiang Chen, Feifei Dou, Ruihan Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Mathematics University of Waterloo Canada National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads. © 2023 ACM.

关键词： Deep neural networks

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A comparative analysis of MQTT and IoT application protocols 2

A comparative analysis of MQTT and IoT application protocols

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： Al Enany, Marwa O Harb, Hany M. Attiya, Gamal Ministry of Higher Education Higher Institute of Computer Science and Information System Computer Science Department Egypt Al-Azhar University Faculty of Engineering Systems and Computers Department Cairo Egypt Menoufia University Faculty of Electronic Engineering Computer Science and Engineering Department Egypt

ISBN: (纸本)9781665418423

Choosing the appropriate IoT protocol for a certain application was and still a critical issue for IoT applications, as protocols vary in behavior in different applications and under different network status. IoT application protocols have a recent focus of attention especially those protocols for the low-powered IoT applications and constrained devices. A brief survey of IoT layers and applied protocols for each layer and their applications were presented, besides, a detailed survey of IoT protocols with a presentation of the main researches that have been done to evaluate their performance. As a use case for this comparative study, the MQTT protocol was chosen because of its simplicity and its publish /subscribe model to be evaluated and compared against other application protocols. © 2021 IEEE.

关键词： Surveys

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A New Multisource Inverter Topology for Electrical Vehicle Applications Controlled by Model Predictive 48

A New Multisource Inverter Topology for Electrical Vehicle A...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Hosseinzadeh, Mohammad Ali Sarebanzadeh, Maryam Garcia, Cristian Babaei, Ebrahim Jolfaei, Alireza Rodriguez, Jose Kennel, Ralph Universidad de Talca Faculty of Engineering Talca Chile Technical University of Munich Chair of High-Power Converters System Munich Germany University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran Flinders University College of Science and Engineering AdelaideSA Australia Universidad San Sebastian Faculty of Engineering Santiago Chile

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

A Multisource Inverter (MSI) comprises several DC sources in the input that can be combined together with varying voltage levels to operate at different loads to reduce the battery size of electric vehicles. Different multisource inverter configurations have been presented recently in the literature. These structures use two DC sources to operate at different demand loads by using a low battery size. The weakness of these multisource inverters is that they use a high number of power switches. In addition, these structures cannot connect two DC used sources together in series to operate under a heavy load, which increases the battery's size, increases high power losses, and reduces efficiency due to a high number of switches. This paper proposes a new topology for multisource inverters that reduces the power electronics switches and the battery size due to generating four combinations between the two used DC sources in the proposed technique. The proposed multisource topology is controlled by the model predictive due to its popular advantages. The performance of the proposal is verified through simulation results in Matlab. The results show that the MPC is a good alternative for such applications due to its simplicity, high performance, and low harmonic content. © 2022 IEEE.

关键词： Topology

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Manufacturing A Low-Cost Telegram and Optical Character Recognition-Based Indoor Air Quality Monitoring Data Logger 8

Manufacturing A Low-Cost Telegram and Optical Character Reco...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Pramono, Andi Budiman, Satrio Arif Farchan, Muchammad Mappaturi, Andi Baso School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia Brawijaya University Information System Computer Science Faculty Malang65145 Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Electrical Engineering Surabaya60111 Indonesia Islamic State University of Maulana Malik Ibrahim Faculty of Science and Technology Architecture Department Malang Indonesia

ISBN: (纸本)9798350326093

Urban air is frequently contaminated with CO, CO2, VOC, HCHO, PM 2.5, and PM 10. Rural regions are at a lower risk than those near roads and industrial areas that produce emissions. Air pollutants negatively affect its inhabitants' health, particularly the respiratory system. Most people will spend more time indoors, requiring a method for monitoring indoor air quality (IAQ). Urban communities need IAQ monitoring instruments as an early warning system for their health. This research seeks to produce inexpensive IAQ that can be utilized by all levels of society, particularly urban communities. This research utilizes a qualitative, design-thinking-based methodology. The research diverged from Empatize, which asserts that urban communities require a method for determining the local air quality. Moreover, it is determined at the define stage that IAQ monitoring, which includes a data log feature, is still comparatively expensive, so a solution is developed at the ideate stage. Creating a prototype is the next step in realizing the specified solution. In this investigation, they decided to use IAQ monitoring, which is relatively inexpensive but lacks log data storage. The author uses the ESP32-cam microcontroller to capture the screen display device, and the resulting image is sent to a telegram and processed with optical character recognition (OCR) to generate a data log. The results of prototype testing are utilized for prototype development. © 2023 IEEE.

关键词： Industrial emissions

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Cost-Effective Network Packet Manipulation and Error-Detection Device

Cost-Effective Network Packet Manipulation and Error-Detecti...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Alexander F. K. Janus Steffen Moser Frank Slomka Faculty of Engineering Computer Science and Psychology Institute of Embedded System/Real-Time Systems School of Advanced Professional Studies Ulm University Ulm Germany

Real-time multimedia communication applications like video conferencing and screen sharing tools are an integral part of our daily communications. These time-critical services are handled via the Internet, a packet-switched and essentially best-effort network, not explicitly designed for robust real-time communication. From a video conferencing service provider's perspective, network issues cause a lot of user complaints due to various reasons: lossy WLAN channels, poorly configured home routers, or crowded carrier-grade network address translation devices. All of these issues have in common that they are often time-variant and non-deterministic. Furthermore, the end-to-end transmission in these highly inhomogeneous networks often suffers from a mixture of more than one error source. To enable a detailed and impartial analysis of multimedia service quality, it is necessary to analyze packets in defective online scenarios and introduce a broadly similar error pattern in an otherwise stable lab network. In this paper, we present SEDER, a hardware-software design of a layer two transparent Ethernet bridge that can be used for two purposes: error detection and error recreation. Implemented on low-cost hardware, the bridge can be easily deployed anywhere to allow debugging, even at a user's site. It is possible to parameterize our bridge to imitate almost all possible network effects, including complex combinations of more than one effect. In addition, we present two case studies, firstly emphasizing the capabilities of SEDER and secondly showing the need to use SEDER to harden open source Video Conferencing Tools against errors.

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Myorcadial Infarction Classification with 15 Lead Electrocardiogram (ECG) Signal using Hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BILSTM)

Myorcadial Infarction Classification with 15 Lead Electrocar...

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International Conference on Informatics and Computing (ICIC)

作者： Ahmad Haidar Mirza Siti Nurmaini Radiyati Umi Partan Muhammad Izman Herdiansyah Faculty Of Engineering Universitas Sriwijaya Faculty Of Computer Science Universitas Bina Darma Palembang Indonesia Intelligent System Research Group Faculty of Computer Science University of Sriwijaya Palembang Indonesia Faculty of Medicine Universitas Sriwijaya Palembang Indonesia Faculty Of Computer Science Universitas Bina Darma Palembang Indonesia

More than three branches of death due to cardiovascular disease. Myocardial infarction (MI) is one of the most dangerous forms of coronary heart disease, with the highest mortality rate among other types of coronary heart disease. MI is caused by reduced oxygen demand in heart muscle tissue when oxygen is pumped throughout the body. To detect MI, experts can use ECG signals. ECG is a tool that can be used to record the activity of biological signals formed due to the electrical activity of the heart. Various studies have been conducted using deep learning to help detect and predict MI. This research proposes a hybrid model of CNN-LSTM and CNN-BILSTM model techniques to predict MI. The MI dataset from the Physiobank (PTB) ECG database consisting of 52 normal subjects and 148 patients was used for training models. The proposed deep learning model uses automatic feature extraction. This study suggests a simple deep learning architecture with automatic feature extraction to learn from data. Evaluation of model performance resulted in an average accuracy of 98.80% for the CNN-LSTM model and 99.90% for the CNN-BILSTM model.

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

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computers, Materials and Continua 2025年第3期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW 2007 Australia School of Data Science and Engineering Indian Institute of Science Education and Research Bhopal 462066 India Research Institute of Marine Systems Engineering Seoul National University Seoul 08826 Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad 201204 India Battery System Modelling Department CLARIOS VARTA Hannover GmbH Hanover 30419 Germany Science and Innovation Group Bureau of Meteorology Brisbane 4000 Australia

Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced prediction times. The GRU achieves the best overall performance for SOC estimation, attaining an R 2 of 0.9999, while the BiDirRNN provides a marginally lower error at a slightly higher computational speed. In contrast, Convolutional Neural Networks (CNN) and Radial Basis Function Networks (RBFN) exhibit relatively high error rates, making them less viable for real-world battery management. Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds, limiting the risk of overcharging or deep discharging. These findings highlight the trade-off between accuracy and computational overhead, offering valuable guidance for battery management system (BMS) designers seeking optimal performance under constrained resources. Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’ robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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