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International Conference on systems and Informatics (ICSAI)

作者： Y. Xiao L. Ma K. Khorasani Dept. of Management & Information Systems Prefectural University of Hiroshima Hiroshima Japan Dept. of Electrical & Computer Engineering Concordia University Montreal Canada

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

In this paper, a new hybrid adaptive nonlinear filter (HANF) scheme with variable step sizes (VSS) is proposed for solar radiation prediction. Our methodology consists of a Volterra filter and a functional link artificial neural network (FLANN) filter. The Volterra filter with the first- and second-order kernels is included, that is capable of expressing both linearity and nonlinearity. The FLANN filter can handle the nonlinearity that may be expressed by higher-order exponential terms that the Volterra filter with a limited number of kernels is unable to deal with. The VSSs are introduced in the two filters to allow the HANF to enjoy desirable tracking capability such that the time-varying nonlinearity underlying the nonlinear phenomena can be detected and tracked. The proposed VSS-HANF is applied to a real hourly solar radiation time sequence to confirm its improved prediction performance as compared to its counterpart with fixed step sizes.

关键词： Accuracy Finite impulse response filters Adaptive filters Weather forecasting Linearity Nonlinear filters Artificial neural networks Solar radiation Kernel Informatics

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An Ising Machine-Based Hybrid Optimization Method Using Constraint Conversion and Correction

An Ising Machine-Based Hybrid Optimization Method Using Cons...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Kinya Iwata Masashi Tawada Nozomu Togawa Dept. of Computer Science and Communications Engineering Waseda University Green Computing Systems Research Organization Waseda University

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This paper proposes an Ising machine-based hy-brid optimization method to solve combinatorial optimization problems, which consists of an approximate formulation process, an annealing process with an Ising machine, and a correction process. The approximate formulation and correction aim to improve the performance of the Ising machine. Computational experiments were conducted on the multi-dimensional knapsack problem (MKP) using an Ising machine. We compare the results with those of the proposed method and the conventional method and demonstrate the effectiveness of the proposed approach.

关键词： Annealing Optimization methods Consumer electronics

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Generalized Stochastic Petri Net Based Simulation of IoT Supported Dynamic Navigation in Teaching Building Evacuation

Generalized Stochastic Petri Net Based Simulation of IoT Sup...

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2022 International Conference on Cyber-Physical Social Intelligence, ICCSI 2022

作者： Strobing, Jordan Granit, Meghan Wang, Jiacun Zhao, Lin Monmouth University Dept. of Computer Science and Software Engineering NJ United States School of Safety Science and Engineering Xi'an Univ. of Sci. and Tech Xi'an China

ISBN: (数字)9781665498357

ISBN: (纸本)9781665498357

Emergency management and evacuation efficiency is important to ensure the safety of faculty and students in college. Teaching buildings are typically of multiple stories. When classes are in session, a teaching building may have a large number of students inside. In case of an event like a fire, people have to be evacuated as soon as possible. Due to panic, people may not use good judgement to choose optimal evacuation path, which can further cause congestion in a path to an exit. This study attempts to leverage the recent advances in information technology to dynamically guide evacuees. We use generalized stochastic Petri nets (GSPN) to model the evacuation process in a teaching building. The layout of the building, sizes of classrooms and hallways, number of people in each room, and people's decision pattern in choosing a direction to move are all parameters of the model. With simulation we can estimate the evacuation time-span. Moreover, by observing the state of GSPN model, we can analyze the congestion status of each simulated pathway, and based on that we can dynamically notify people to select the right path that lead them to an exit with the least amount of time. © 2022 IEEE.

关键词： Internet of things

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Robust Active and Reactive Power Control by using Single-Phase Grid connected H-Bridge Inverter

Robust Active and Reactive Power Control by using Single-Pha...

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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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Design and Development of a Smart Tool for Distribution Transformer sizing Considering Plug-in Electric Vehicles Charging

Design and Development of a Smart Tool for Distribution Tran...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Yussef Afifi Chantel Le Elias Louis Leontis Yuanrong Kyle Zheng Walid G. Morsi Dept. of Electrical Computer and Software Engineering Faculty of Engineering Ontario Tech University Oshawa Canada Faculty of Engineering Ontario Tech Universit Oshawa Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

The use of electric vehicles (EV) has exponentially grown within the past couple of years and it is expected to increase by 2050 causing a drastic increase in residential demand. Due to such an increase in demand, many existing distribution transformers servicing these residential homes may need either replacement or upgrade in their sizes to account for the home charging of these EVs. Choosing the correct economic transformer’s size helps electric utilities to benefit from such a significant capital investment. In this paper, a Smart Transformer Sizing Tool (STST) was developed to provide the correct economic size of transformers that ensure no overload as well as maintain the voltage quality. The results demonstrating the effectiveness of the proposed tool are presented and conclusions are drawn.

关键词： Economics Plug-in electric vehicles Heat engines Knowledge based systems Voltage Transformers Power industry

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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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