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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Resul Coteli Korhan Kayisli Ruhi Zafer Caglayan Dept. of Energy Systems Engineering Technology Faculty Firat University Elazig Turkiye Dept. of Electrical- Electronic Engineering Engineering Faculty Gazi University Ankara Turkiye Dept. of Computer Engineering Engineering Faculty TED University Ankara Turkiye

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

In a single-phase power network, inverters can be used to control the active and reactive power between sources. This task can be achieved by proper control of the current of the single-phase inverter. The current controllers using dq-frame components have a wide application area because of their superior features such as zero steady-state error and using the DC quantities. In this study, the active and reactive power between sources in a single-phase power system consisting of an AC voltage source and a DC voltage source are controlled via dq-frame based super twisting sliding mode (ST-SMC) current controller. For this aim, a Matlab/Simulink model is constructed. The model includes single phase H-bridge inverter, DC voltage source, AC voltage source, AC load, LCL filter and control unit. Two super twisting sliding mode controllers are used to control d and q-axis currents. Decoupling terms are added to output of each controller for independent control of active and reactive power. The simulation studies are performed to demonstrate the controller's ability to provide/absorb active and reactive power. To demonstrate the ability to provide/absorb active power, the d-axis current command is set to 10 A up to 2 s from the start of the simulation, and then, it is set to – 10 A between 2 s to 4 s. To demonstrate the ability to provide/absorb reactive power, the q-axis current command is set to 10 A up to 2 s from the start of the simulation, and then, it is set to – 10 A between 2 s to 4 s. The simulation results show that the controller provides/absorbs the desired active and reactive power quickly and without steady-state error.

关键词： Reactive power Reactive power control Simulation Inverters Mathematical models Steady-state Smart grids

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Multi-Point Positional Insertion Tuning for Small Object Detection

arXiv

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arXiv 2024年

作者： Goto, Kanoko Karasawa, Takumi Hirose, Takumi Kawakami, Rei Inoue, Nakamasa Dept. of Computer Science Institute of Science Tokyo Tokyo Japan Dept. of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan

Small object detection aims to localize and classify small objects within images. With recent advances in large-scale vision-language pretraining, finetuning pretrained object detection models has emerged as a promising approach. However, finetuning large models is computationally and memory expensive. To address this issue, this paper introduces multi-point positional insertion (MPI) tuning, a parameter-efficient finetuning (PEFT) method for small object detection. Specifically, MPI incorporates multiple positional embeddings into a frozen pretrained model, enabling the efficient detection of small objects by providing precise positional information to latent features. Through experiments, we demonstrated the effectiveness of the proposed method on the SODA-D dataset. MPI performed comparably to conventional PEFT methods, including CoOp and VPT, while significantly reducing the number of parameters that need to be tuned. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Analysis of Defect Associated with Powder Bed Fusion with Deep Learning and Explainable AI 15

Analysis of Defect Associated with Powder Bed Fusion with De...

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15th International Conference on Knowledge and Smart Technology, KST 2023

作者： Pratap, Ayush Sardana, Neha Utomo, Sapdo John, A. Karthikeyan, P. Hsiung, Pao-Ann Indian Institute of Technology Dept. of Metallurgical and Materials Engineering Ropar Rupnagar India National Chung Cheng University Grad. Ins. Ambient Intelligence and Smart Systems Chiayi County Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan

ISBN: (纸本)9781665477123

Research into the detection, classification, and prediction of internal defects using surface morphology data of parts created via powder bed fusion-type additive manufacturing has become a hot topic in the previous decade thanks to the development of deep learning. However, there is no other evidence to evaluate the model other than accuracy and metrics. In this paper, a novel data set is compiled from various literature and other sources to evaluate the black box model using explainable artificial intelligence (XAI). The data set contains three major powder bed fusion defects: gas porosity, lack of fusion, and balling. The anomaly was initially found using convolutional neural networks (CNN) and transfer learning. Based on test data, a model comparison was performed to determine the best accuracy and an F1 score. VGG16 has outperformed all other models in terms of accuracy, with an F1 score of 98.6 percent. Further, the model has been compared with the existing state-of-the-art model for classification in the domain of powder bed fusion defects. Finally, VGG16 was employed to interpret and explain the test data set. The LIME explanations revealed that the feature predicted by the model was present in conjunction with the fault. As a result, we are confident that the proposed model with XAI would considerably improve the fairness and trustworthiness of the output result in the powder bed fusion field. This can also aid in the automation of additive manufacturing in the realm of Industry 4.0. © 2023 IEEE.

关键词： computer vision

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Addressing Misinformation in Bengali Media: A Hybrid Deep Learning Solution

Addressing Misinformation in Bengali Media: A Hybrid Deep Le...

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International Conference on computer and Information Technology (ICCIT)

作者： Fuad Ibne Jashim Farhad Shah Imran Md Mehedi Hasan Santo Mahbub Khan Anamul Sakib Md Shahidur Rahman Md Ariful Islam Rezaul Haque Shafiur Rahman Dept. of Information Technology CQ University Australia Dept. of Computer Science and Engineering BRAC University Bangladesh Dept. of Management Information System Pacific State University USA Dept. of Business Administration International American University USA Dept. of Engineering Management Westcliff University USA Dept. of Computer Science and Engineering East West University Bangladesh Dept. of Software Engineering Daffodil International University Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

The spread of fake news poses significant threats to society, democracy, and public discourse. This is especially true in Bengali-speaking communities where digital literacy and language-specific resources are limited. To address this issue, we urgently need effective ways to detect and mitigate fake news in the Bengali language. This paper highlights the unique challenges associated with linguistic diversity and misinformation prevalence. Our research introduces a hybrid deep learning algorithm specifically designed to classify fake and authentic Bengali news articles. The methodology involves extensive data collection, rigorous preprocessing to improve textual quality, and the use of advanced feature extraction methods. We combined and trained CNN and LSTM/BiLSTM models to handle the nuances of Bengali text. Our proposed algorithm significantly outperforms existing models, with a high accuracy of 98.45% in distinguishing between fake and authentic news and reducing false positives. Additionally, we developed a user-friendly web application that allows general public to input news articles and obtain predictions on their authenticity in real-time. This study not only advances the field of fake news detection but also provides essential tools for journalists, policymakers, and the general public to combat misinformation in Bengali media. Our findings have broader implications, promoting informed public discourse and supporting the development of resilient digital ecosystems. This underscores the need for ongoing research and adaptation of such models to combat the global challenge of fake news in various languages and contexts.

关键词： Deep learning Machine learning algorithms Social networking (online) Biological system modeling Scalability Ecosystems Text categorization Classification algorithms Cultural differences Fake news

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Comparative Analysis of the Queueing systems E2/M/1 and M/E2/1 with Shifted Distribution Laws

Comparative Analysis of the Queueing Systems E2/M/1 and M/E2...

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2020 IEEE International Conference on Problems of Infocommunications Science and Technology, PIC S and T 2020

作者： Bakhareva, Nadezhda Tarasov, Veniamin Povolzhskiy State University of Telecommunications and Informatics Dept. of Info. and Computer Engineering and Software and Management in Technical Systems Department Samara Russia

ISBN: (纸本)9781728191775

The objective of this work is to the output the solution in closed form for the average waiting time in a queue for queueing systems with shifted Erlangian and exponential input distributions and comparative analysis of their results with the results for conventional systems. As is known from queuing theory, the average waiting time for requirements in a queue is the main characteristic of a queuing system. To solve this problem, we used the classical method of spectral decomposition of the solution of Lindley integral equation, which allows one to obtain a solution for average the waiting time for systems under consideration in a closed form. The method of spectral decomposition of the solution of Lindley integral equation plays an important role in the theory of systems G/G/1. For the practical application of the results obtained, the well-known method of moments of probability theory is used. The introduction of the time shift parameter in the laws of input flow distribution and service time for the systems under consideration turns them into systems with a delay with a shorter waiting time. This is because the time shift operation reduces the coefficient of variation in the intervals between the receipts of the requirements and their service time, and as is known from queuing theory, the average wait time of requirements is related to these coefficients of variation by a quadratic dependence. The obtained results expand and supplement the well-known incomplete formula of the queuing theory for the average waiting time of requirements in the queue for G/G/1 systems. © 2020 IEEE.

关键词： Integral equations

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Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting

arXiv

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arXiv 2024年

作者： Oh, Jiyong Raza, Syed M. Mwasinga, Lusungu J. Kim, Moonseong Choo, Hyunseung Dept. of Artificial Intelligence Sungkyunkwan University Suwon Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of IT Convergence Software Seoul Theological University Bucheon Korea Republic of

Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emergingfield with numerous applications. Operator-controlled UAV flights and manualstatic flight configurations are major limitations for the wide adoption ofscalability of UAV services. Several services depend on excellent UAVconnectivity with a cellular network and maintaining it is challenging inpredetermined flight paths. This paper addresses these limitations by proposinga Deep Reinforcement Learning (DRL) framework for UAV path planning withassured connectivity (DUPAC). During UAV flight, DUPAC determines the bestroute from a defined source to the destination in terms of distance and signalquality. The viability and performance of DUPAC are evaluated under simulatedreal-world urban scenarios using the Unity framework. The results confirm thatDUPAC achieves an autonomous UAV flight path similar to base method with only2% increment while maintaining an average 9% better connection qualitythroughout the flight. © 2024, CC BY.

关键词： Reinforcement learning

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The Design of a Lightweight Inductive Power Coupler for eVTOLs

The Design of a Lightweight Inductive Power Coupler for eVTO...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Duleepa J Thrimawithana Kai-Yeung Li Jackman Lin Didier Chassaigne Olivier Crepel Madalina Pascaru Antoine Van der Laan Julien Gosteau Grant Covic Dept. of Electrical Computer Software Engineering The University of Auckland New Zealand Airbus Central R&T France

ISBN: (数字)9798350351156

ISBN: (纸本)9798350351163

Inductive power transfer (IPT) technology can play an important role in achieving the sustainability goals of the aviation industry. However, the power-to-weight ratio of the IPT power pick-up system needs to improve significantly to beyond 10 kW/kg to improve feasibility for applications in the aviation sector. This paper presents a new magnetic coupler design that would help achieve this power-to-weight target in an IPT system with small airgaps. The proposed design introduces an air cavity between the coil and the ferrite structure of the magnetic coupler on the eVTOL/aircraft side. The ferrite structure in this design functions somewhat like a magnetic shield, and the air cavity helps reduce the flux density in the ferrite while also making the flux distribution more uniform. A comparison between traditional and proposed designs is provided to show that a 40% weight reduction can be achieved. Experimental data of a 90 kW prototype is presented to show that it can operate at an efficiency of 97.4% when transferring power over a 10 mm coil-coil air-gap.

关键词： Ferrites Wireless communication Magnetic flux Air gaps Prototypes Couplers Magnetic noise Power electronics Sustainable development Magnetic shielding

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Movement Attentive Graph Embedding for Improving Next Point of Access Prediction

Movement Attentive Graph Embedding for Improving Next Point ...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Honggu Kang Taesoo Kim Huigyu Yang Hyunseung Choo Dept. of AI Systems Engineering Sungkyunkwan University Suwon South Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea Dept. of Superintelligence Engineering Sungkyunkwan University Suwon South Korea

Next Point-of-Attachment (PoA) prediction is a deep learning-based approach to provide seamless connectivity in the mobile networks from heterogenous and dense user access. Existing recurrent neural models utilize time series characteristics to extract and learn the sequential association changes occurs between all Base Stations (BSs). Since the user moves from certain BS to its adjacent BS, the deployments of these BSs are a crucial feature for improving prediction accuracy. To this end, we propose a Graph Embedding - Long Short-Term Memory (GE-LSTM) model using Recurrent Neural Network (RNN) and Graph Neural Network (GNN) jointly. The time-series pattern of the user movement is extracted by LSTM, and the movement between adjacent BSs is emphasized as GNN through GE. The joint property of the proposed model is implemented through mixture-of-experts approach from GNN and LSTM. The performance is evaluated along with the RNN families and Transformer models, furthermore, it explores the significance of the graph-based mobility embeddings. In our experiment, the proposed model outperforms other SOTA models by achieving the maximum accuracy 94%.

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Signal Processing Techniques from Accelerometers on Railway Transport Based on the Wavelet Transform

Signal Processing Techniques from Accelerometers on Railway ...

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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Alexander M. Boronakhin Alexandra V. Bolshakova Dmitry M. Klionskiy Daniil Yu. Larionov Roman V. Shalymov dept. of Laser Measurement and Navigational Systems faculty of Information Measurement and Biotechnical Systems Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation dept. of Laser Measurement and Navigational Systems Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation dept. of Information Systems dept. of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation

Railway transport safety is directly connected with the condition of railway tracks and also the condition of railway wheels’ surface. One of the main reasons for abnormal situations on railways are various defects such as irregularities of a railway track. Thus, we need to estimate the length of short and impulse irregularities. It is also important to carry out joint analysis of vibration acceleration signals recorded by accelerometers in order to study types and sizes of railway track irregularities.

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A Dynamic Cooperative Multi-Agent Online Coverage Path Planning Algorithm 16

A Dynamic Cooperative Multi-Agent Online Coverage Path Plann...

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16th International Conference on computer engineering and systems, ICCES 2021

作者： Sadek, Mina G. El-Garhy, Ahmed M. Mohamed, Amr E. Helwan University Faculty of Engineering Computer Systems Engineering Dept. Cairo Egypt

ISBN: (纸本)9781665408677

This paper proposes a novel approach for finding an optimized solution for the online coverage path planning in unknown environments problem employing cooperative multi robotic agents. The suggested approach lessens the time required for solving the coverage problem by using cooperative multiple robotic agents working together to achieve enhanced complete coverage while sustaining minimum time, and the minimum number of repeated cells and respecting the service time. The distributed multi-agent planning with coordination process is optimized using dynamic programming with a rolling horizon limited look-ahead policy for online capability. In addition to expanding the usual single-agent action tree to hold a multi-agent scheme for possible collision-free sub-trajectories generation con-cerning quality, repetitions count, length per single agent to plan and execute cooperatively for covering a given unknown sur-roundings. Regarding coverage optimization, we utilize a multi-objective optimization genetic based algorithm with uniform cost search for the single-agent coverage sub-trajectory planning. In multi-agent-based systems, communication introduces overhead and have a direct influence on the system's robustness. Hence, we proposed a voting and coalition formation based on the K-means clustering algorithm for minimizing the communication henceforth improving the system's robustness. Simulation results highlight an enhancement regarding speeding up the complete coverage process compared to optimum single-agent approaches while advancing near-optimal utilization and efficiency minimizing repetitions in different unknown environments. © 2021 IEEE.

关键词： Multiobjective optimization

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