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International Conference on European Electricity Market, EEM

作者： Volkan S. Ediger Mehmet Ali Küçüker Istemi Berk Ali İnan Fehmi Görkem Üçtuğ Center for Energy and Sustainable Development (CESD) Kadir Has University İstanbul Türkiye Department of Environmental Engineering İzmir Institute of Technology İzmir Türkiye Department of Economics Faculty of Business Dokuz Eylül University İzmir Türkiye Computer Engineering Department Adana Alparslan Türkeş Science and Technology University Adana Türkiye Department of Mechanical Engineering İzmir University of Economics İzmir Türkiye

ISBN: (数字)9798350381740

ISBN: (纸本)9798350381757

Organized Industrial Zones (OIZ), which gained legal status by Law 4562 of 2000, played a significant role in Turkish industrialization policies, particularly in improving Small and Medium-sized Enterprises (SMEs). The energy management (EM) within OIZs is essential for Türkiye's green transition and 2053 net-zero pathway. Following the publication of a directive on OIZ's electricity market activities in 2006, enterprises can purchase electricity directly from OIZ management. Moreover, the Energy Efficiency Law No. 5627 of 2007 required OIZs to establish an energy management unit (EMU) to serve the participants with less than 1000 tons of oil equivalent (toe) energy consumption. EMUs provide OIZ management with a unique opportunity to enhance sustainable energy transition by increasing renewable energy production and improving the energy efficiency of participating enterprises. The primary goal of this research is to evaluate the effectiveness of energy management units in OIZs in encouraging energy efficiency and green energy transition in the Turkish manufacturing industry. As a case study, we examine EM in the Adana Hacı Sabancı Organized Industrial Zone (Adana OIZ), which ranks third among OIZs regarding electricity consumption. We analyze data on electricity infrastructures, roof-top PVs, invoice settlements/offsets, energy efficiency investments, and GHG emissions between 2017 and 2023. Our preliminary findings suggest that EMU in the Adana OIZ makes a very important contribution to the green transition of industrial establishments and that regulatory changes over the last decades have had positive effects. The share of renewable energy in the total energy mix increased from 1.6% to 21.4% over six years, and there has been a noteworthy enhancement in energy efficiency, reaching 27% in 22 companies evaluated. The main policy implication of our findings is that the role of regulatory bodies and efficient energy management in OIZs will be critical in achieving Tü

关键词： Manufacturing industries Green energy Law Electricity Net zero Oils Green products

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Metaheuristic conditional neural network for harvesting skyrmionic metastable states

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Physical Review Research 2023年第4期5卷 043199-043199页

作者： Qichen Xu I. P. Miranda Manuel Pereiro Filipp N. Rybakov Danny Thonig Erik Sjöqvist Pavel F. Bessarab Anders Bergman Olle Eriksson Pawel Herman Anna Delin Department of Applied Physics School of Engineering Sciences KTH Royal Institute of Technology AlbaNova University Center SE-10691 Stockholm Sweden Swedish e-Science Research Center (SeRC) KTH Royal Institute of Technology SE-10044 Stockholm Sweden Department of Physics and Astronomy Uppsala University Box 516 SE-75120 Uppsala Sweden School of Science and Technology Örebro University SE-701 82 Örebro Sweden Department of Physics and Electrical Engineering Linnaeus University Hus Magna SE-39231 Kalmar Sweden Science Institute University of Iceland 107 Reykjavík Iceland Division of Computational Science and Technology School of Electrical Engineering and Computer Science KTH Royal Institute of Technology AlbaNova University Center SE-10691 Stockholm Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) KTH Royal Institute of Technology SE-10044 Stockholm Sweden

We present a metaheuristic conditional neural-network-based method aimed at identifying physically interesting metastable states in a potential energy surface of high rugosity. To demonstrate how this method works, we identify and analyze spin textures with topological charge Q ranging from 1 to −13 (where antiskyrmions have Q<0) in the Pd/Fe/Ir(111) system, which we model using a classical atomistic spin Hamiltonian based on parameters computed from density functional theory. To facilitate the harvest of relevant spin textures, we make use of the newly developed segment anything model. Spin textures with Q ranging from −3 to −6 are further analyzed using finite-temperature spin-dynamics simulations. We observe that for temperatures up to around 20 K, lifetimes longer than 200 ps are predicted, and that when these textures decay, new topological spin textures are formed. We also find that the relative stability of the spin textures depend linearly on the topological charge, but only when comparing the most stable antiskyrmions for each topological charge. In general, the number of holes (i.e., non-self-intersecting curves that define closed domain walls in the structure) in the spin texture is an important predictor of stability—the more holes, the less stable the texture. Methods for systematic identification and characterization of complex metastable skyrmionic textures—such as the one demonstrated here—are highly relevant for advancements in the field of topological spintronics.

关键词： Image processing Magnetic texture Optimization problems Density functional theory Neural network simulations Skyrmions

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Deep Learning Networks for Human Knee Abnormality Detection Based on Surface EMG Signals

Deep Learning Networks for Human Knee Abnormality Detection ...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

Early knee problem management relies on precise identification and classification of abnormalities. Surface electromyography (sEMG) and goniometer signals offer non-invasive screening for muscle activity and joint angle patterns, yet their complexity poses challenges in extracting critical diagnostic information. This paper proposes a novel deep-learning method using sEMG and goniometer data for knee abnormality diagnosis. The proposed ResNeXt model, employing CNNs and multi-kernel modules, is evaluated on the UCIEMG dataset. Experimental results demonstrate ResNeXt's superior accuracy, precision, recall, and F1-score compared to baseline models (CNN and LSTM). Res NeXt achieves the best performance with combined EMG and goniometer data, reaching 96.37% accuracy and 93.77% F1-score, with fewer trainable parameters, indicating computational efficiency. The findings indicate ResNeXt's effectiveness in identifying knee abnormalities using biosensor data, particularly sEM G and goniometer signals, aiding early disease detection and treatment.

关键词： Deep learning Accuracy Goniometers Signal processing Muscles Electromyography Telecommunications Surface treatment Long short term memory Diseases

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Compact, Hybrid Slot-Dipole-Monopole Wi-Fi 7 Antenna for Slim-Bezel Notebook Applications

Compact, Hybrid Slot-Dipole-Monopole Wi-Fi 7 Antenna for Sli...

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Asia-Pacific Conference on Microwave

作者： M. Idrees Magray Saou-Wen Su Jui-Han Lu Antenna Design Department Advanced EM & Wireless Comm R&D Center ASUSTek Computer Inc Taipei Taiwan Department of Electronic Communication Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

A hybrid slot-dipole-monopole antenna is demonstrated that is suitable for Wi-Fi 7 notebook applications. The design consists of an open slot, a coupled strip resonator, and a T-shaped feed, created on the two layers of the substrate. The open slot is able to operate in the 2.4 GHz (2400-2484 MHz) wireless local area network (WLAN) band. The coupled strip resonator and the T-shape feed together generate two resonant dipole-monopole modes that cover the 5 GHz (5150-5825 MHz) and 6 GHz (5925-7125 MHz) WLAN bands respectively. The design is simple in structure and occupies a compact size of $4.6 \mathrm{~mm} \times 15 \mathrm{~mm}$ (approximately $0.037 \lambda \times 0.120 \lambda$ at 2.4 GHz), which can find practical applications in slim-bezel notebook computers.

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Jamming Mitigation in JRC Systems via Deep Reinforcement Learning and Backscatter-supported Intelligent Deception Strategy 6

Jamming Mitigation in JRC Systems via Deep Reinforcement Lea...

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6th IEEE International Conference on computer and Communication Systems, ICCCS 2021

作者： Ismail, Lotfi Niyato, Dusit Sun, Sumei Hoang, Dinh Thai Kim, Dong In Liang, Ying-Chang School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute for Infocomm Research Astar Singapore Singapore School of Electrical and Data Engineering University of Technology Sydney SydneyNSW Australia Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Center for Intelligent Networking and Communications University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9780738126043

In this paper, we develop a framework to optimize the trade-off between radar sensing and data transmission in Joint Radar-Communication (JRC) systems under smart and reactive jamming attacks. First, we propose a novel JRC design architecture that uses backscatter technology and deception strategy to leverage jamming attacks for a JRC system. The deception strategy is used to predict the jammer's action and adopt appropriate counterattack instantaneously, while backscatter technology is used to transmit data on the jamming signals. To deal with the jamming strategy uncertainty (e.g., jamming capability), we then develop a deep reinforcement learning algorithm to quickly find the optimal defense policy for the JRC system. Our in-depth investigation reveals that the proposed design not only significantly undermines the jamming attacks, but also utilises jamming signals to improve the system performances. Compared with conventional anti-jamming methods, our proposed design significantly improves data throughput while maintaining a satisfactory radar sensing performance in dynamic environments. Moreover, the proposed deep reinforcement learning based solution can converge four times faster than a conventional deep Q- Learning based solution. © 2021 IEEE.

关键词： Deep learning

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Human Activity Recognition and Payload Classification for Low-Back Exoskeletons Using Deep Residual Network

Human Activity Recognition and Payload Classification for Lo...

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Research, Invention, and Innovation Congress (RI2C)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

Exoskeletons created by robotics currently hold significant potential for improving the well-being and quality of life for manufacturing laborers. However, the effectiveness of existing solutions is often hindered by inadequate control systems. The limited adoption of exoskeletons in occupational settings can be attributed to their inability to adapt to users and tasks, which is a result of the constantly changing circumstances in which they are utilized. To address this issue, wearable sensors capable of being carried on the human body can employ learning-based methodologies to anticipate and classify user motion characteristics by training on extensive datasets. This study introduces a deep learning approach that utilizes inertial sensors to equip manufacturing exoskeletons with the ability to recognize human movements and adjust for payload compensation. Inertial measurement equipment can be conveniently integrated into manufacturing exoskeletons through either wearable or embeddable means. A deep residual network was employed for human action identification and categorization of lifted object weights up to 15 kg. The study achieved an average F1-score of 93.91% for activity identification and 94.35% for payload classification using the provided model. The model was trained and tested on a sample of 12 young, healthy volunteers.

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Multiferroic Dark Excitonic Mott Insulator in the Breathing-Kagome Lattice Material Nb3Cl8

arXiv

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arXiv 2024年

作者： Khan, Mahtab A. Din, Naseem Ud Englund, Dirk Leuenberger, Michael N. NanoScience Technology Center University of Central Florida OrlandoFL United States Department of Physics Federal Urdu University of Arts Sciences and Technology Islamabad Pakistan Department of Physics Florida Atlantic University Boca RatonFL United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States NanoScience Technology Center Department of Physics College of Optics and Photonics University of Central Florida OrlandoFL United States

Motivated by the recent discovery of flat bands (FBs) in breathing Kagome lattices (BKLs), we present a detailed first-principles study of the optical response of single-layer (SL) Nb3Cl8 using the GW-Bethe-Salpeter equation (GW-BSE) method, incorporating self-energy corrections and excitonic effects. Our findings reveal a rich spectrum of strongly bound excitons. The key results are fourfold: (i) SL Nb3Cl8 exhibits a dark spin-triplet Frenkel exciton ground state with binding energy substantially larger than the GW-renormalized band gap, giving rise to a negative exciton energy peak at −0.14 eV and indicating an excitonic Mott insulator phase potentially stable at room temperature (kBT = 0.025 eV);(ii) the brightest exciton peak appears at 1.2 eV, in excellent agreement with experimental optical absorption spectra. (iii) We map the low-energy Frenkel exciton system onto a Hubbard model with spin-1 particles on a triangular lattice, resulting in frustrated spin configurations due to antiferromagnetic spin-spin exchange interaction. (iv) As the spin-triplet Frenkel excitons have electric dipoles that interact with each other via electric dipole-dipole interaction, we obtain antiferroelectric ordering, possibly stable at room temperature. Thus, we propose that Nb3Cl8 is a multiferroic dark spin-triplet excitonic Mott insulator. © 2024, CC BY.

关键词： Ground state

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A Hybrid SARIMAX Model in Conjunction with Neural Networks for the Forecasting of Life Insurance Industry Growth in Thailand

A Hybrid SARIMAX Model in Conjunction with Neural Networks f...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Supika Huadsri Sakorn Mekruksavanich Anuchit Jitpattanakul Wikanda Phaphan Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Research Group in Statistical Learning and Inference

This article is conducted with the primary aim of comparing various forecasting models to identify the optimal model for forecasting the growth of the life insurance industry in Thailand. The models under consideration include the SARIMAX model, the Recurrent Neural Network (RNN), and a hybrid model introduced as RNN-SARIMAX. The dataset utilized for this investigation is extensive, consisting of monthly direct premium data from the Thai life insurance sector spanning January 2003 to December 2022. The dataset is segregated into two distinct segments. Initially, data from January 2003 to December 2021 is employed for constructing the forecasting model. Subsequently, data from January 2022 to December 2022 is utilized for a comparative analysis of the forecasting models, to determine the most suitable and accurate model for predicting the growth of the life insurance industry in Thailand. The evaluation criteria for comparing models include the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R-squared). The consistent findings from our analysis indicate that the SARIMAX model consistently outperforms both the RNN and RNN-SARIMAX models.

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Developing Robotic Arm Control Using Deep Generative Adversarial Network

SSRN

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SSRN 2023年

作者： Hsieh, Yi-Zeng Su, Mu-Chun Xu, Fu-Xiong Chen, Jieh-Haur Department of Electrical Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Department of Computer Science and Information Engineering National Central University Jhongli Taoyuan320317 Taiwan Department of Electrical Engineering National Taiwan Ocean University Keelung202301 Taiwan Department of Civil Engineering Research Center of Smart Construction National Central University Jhongli Taoyuan320317 Taiwan

This study primarily established a system of robotic platforms using deep learning techniques to investigate the distinctions among contemporary deep learning methods like Deep Neural Networks and Convolutional Neural Networks. The Generative Adversarial Network (GAN) was employed as the primary research approach to model robot kinematics, enabling a comparative analysis of pros and cons between GAN and traditional mainstream models in terms of kinematics. The developmental process was partitioned into four key segments: hardware for the robotics platform, control mechanisms, data acquisition, and the application of deep learning to construct the kinematic model. The hardware component encompassed a robotic arm and an arm platform, collectively boasting 10 degrees of freedom and operating within a custom-designed spatial environment. Robot vision relied on an infrared camera from Kinect v2, where a self-developed YOLO v3 model, coupled with the built-in IR projector of Kinect v2, facilitated object detection. This enabled the robot to identify objects' spatial coordinates (x, y, z) within the designated platform. Utilizing joint angles, the robot could then navigate towards the detected object, subsequently executing actions such as grasping, all orchestrated by the proposed GAN-based motion model. © 2023, The Authors. All rights reserved.

关键词： Robotic arms

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Hybrid Attention with CNN-BiLSTM and CBAM for Efficient Wearable Activity Recognition

Hybrid Attention with CNN-BiLSTM and CBAM for Efficient Wear...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

The use of widespread sensors for automatic activity recognition has become a dynamic area of research due to its broad applications in healthcare, fitness monitoring, and assisted living. To efficiently identify daily and sports activities from sensor data, this study suggests a hybrid deep learning model that integrates convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM) networks, and convolutional block attention modules (CBAM). The model starts by extracting spatial features from the sensor data using CNN layers. These spatial features are then employed to offer temporal context information to the BiLSTM network. To conclude, the CBAM attention mechanism is applied to guide the model’s focus to the most informative segments of the BiLSTM feature maps. This approach enables the accurate recognition of patterns in both complex and daily sports activities. The effectiveness of the proposed hybrid model is evaluated using the UCI-DSA, a publicly available benchmark dataset that includes sports and daily activities collected through wearable motion sensors. In contrast to existing approaches, the outcomes reveal that a unified framework involving CNN, BiLSTM, and CBAM achieves outstanding performance in activity recognition, exceeding $99 \%$ in both accuracy and F1-score while maintaining computational efficiency.

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