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Conference on Lasers and Electro-Optics (CLEO)

作者： Seamus O'Hara Joe Costello Qi Tian Qile Wu Ken West Loren Pfeiffer Mark Sherwin Liang Wu Department of Physics and Institute of Terahertz Science and Technology University of California Santa Barbara Santa Barbara CA Intelligence Community Post-Doctoral Research Fellowship Program Philadelphia PA Department of Physics University of Pennsylvania Philadelphia PA Department of Electrical and Computer Engineering Princeton University Princeton NJ

ISBN: (纸本)9798350369311

In this proceeding, we present polarization data of sidebands emitted from strongly driven quasiparticles, demonstrate the dependence of these data on various parameters of the external driving field, and describe the data using an analytical expression, dependent on driving field parameters and the effective Hamiltonian of the system. We propose using this method on spin-momentum locked systems, such as altermagnets, to observe and reconstruct sidebands which are influenced by the topology of individual bands.

关键词： Electro-optic effects Amplitude modulation Topology Interferometry

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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International Conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

Sports Science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport Science recognizes and prevents injuries. Sensors and statistics formalize Sports Science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

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Adversarially Robust Reinforcement Learning for Urban Traffic Signal Control

Adversarially Robust Reinforcement Learning for Urban Traffi...

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International Conference on Technology, Informatics, Management, engineering, and Environment (TIME-E)

作者： Muhammad Shafly Hamzah Endra Joelianto Herman Yoseph Sutarto Adiyana Putri Instrumentation and Control Master Program Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia Department of Intelligent System PT. Pusat Riset Energi Bandung Indonesia Doctoral Program of Engineering Physics Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

Urban traffic congestion presents critical challenges, resulting in delays, environmental pollution, and significant economic costs. While traditional traffic control methods, such as fixed-time and adaptive systems, have been widely implemented, they often fail in unpredictable conditions, particularly when confronted with noisy or inaccurate data. This research proposes a robust reinforcement learning (RL) solution for traffic signal control using the RADIAL-DQN (Robust Adversarial Loss DQN) algorithm. Integrating adversarial training enhances the system's robustness to real-world uncertainties. Evaluations conducted using a SUMO traffic simulation show that the robust agent produced an increase of only 60.71% in queue length under a projected gradient descent (PGD) adversarial attack, outperforming standard RL agents, which experienced an 89% increase. These results demonstrate the potential of robust RL to improve both the reliability and scalability of urban traffic control systems.

关键词： Training Adaptive systems Uncertainty Scalability Urban areas Traffic control Robustness Standards Traffic congestion Testing

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Optimal RTGC Controller using Robust PID H∞ Integral-Backstepping under Payload Mass and Rope Length Uncertainties

Optimal RTGC Controller using Robust PID H∞ Integral-Backst...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Steven Bandong Yul Yunazwin Nazaruddin Endra Joelianto Engineering Physics Doctoral Program Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Indonesia National Center for Sustainable Transportation Technology Institut Teknologi Bandung Indonesia

ISBN: (纸本)9781665476881

The increase in goods traffic between countries and islands requires the availability of port automation so that the distribution of goods is faster and the quality is guaranteed. One of the control problems for automating the port is controlling the position and sway angle of the container on the Rubber Tyred Gantry Crane (RTGC). However, the RTGC system's model is often uncertain because of the uncertainty in the mass of the payload and the length of the hauling rope when operating. This paper proposes a robust PID controller based on $H$ ∞ integral backstepping to overcome this problem. Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Genetic Algorithms (GA) are used to optimize the parameter of $H$ ∞ integral backstepping so that the system response follows the desired trajectory. The results show that the control system designed can provide a satisfactory solution to variations in the mass and length of the rope. PSO and SA give better optimal and robust performance than GA.

关键词： Backstepping Uncertainty Automation Simulated annealing Control systems Robustness Trajectory

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Efficient Removal of Amoxicillin, Ciprofloxacin, and Tetracycline from Aqueous Solution Utilizing Cu-Doped Bi2O3 Material

SSRN

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SSRN 2023年

作者： Sa’adah, Fatkhiyatus Sutanto, Heri Hadiyanto, H. Alkian, Ilham Doctoral Program of Environmental Science School of Postgraduate Diponegoro University Semarang50275 Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang50275 Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University Semarang50275 Indonesia Diponegoro University Semarang50275 Indonesia

This study investigates the effectiveness of the Cu-doped Bi2O3 method for removing antibiotics. Cu at varying concentrations of 0%, 2%, 4%, 6%, and 8% was used to synthesize Bi2O3 material successfully. The optimal results were obtained by adding 4% Cu to the Bi2O3 matrix. Cu-doped Bi2O3 was characterized by an energy band gap of 2.32 eV, a crystal size of 37.04 nm, and α-Bi2O3 and Rouaite phases. Material containing Cu-doped Bi2O3 was used to degrade antibiotics such as amoxicillin (AMX), ciprofloxacin (CIP), and tetracycline (TC). The various removal techniques investigated include adsorption, photolysis, and photocatalysis. The photocatalytic procedure yielded the highest results in terms of degradation efficiency. The removal efficiency of each antibiotic using the photocatalytic method varies, with AMX at 52.06%, CIP at 61.72%, and TC at 69.44%. Cu-doped Bi2O3 degraded TC-type antibiotics more rapidly, with a reaction rate constant of 0.0065 minutes-1, according to the results of the reaction rate constants. The high removal efficiency and rapid reaction rate indicate that Cu-doped Bi2O3 is an effective antibiotic removal agent, especially for tetracycline-type antibiotics. © 2023, The Authors. All rights reserved.

关键词： Antibiotics

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International Conference of Computer and Informatics engineering (IC2IE)

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. Technology also plays an essential role in supporting runners and running races. Technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential.

关键词： Training Roads Artificial intelligence Informatics

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Regenerative Braking System in E-Trike: Interval Type-2 Fuzzy Logic Control to Enhance Driving Range

Regenerative Braking System in E-Trike: Interval Type-2 Fuzz...

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International Conference on Technology, Informatics, Management, engineering, and Environment (TIME-E)

作者： Muhammad Rizalul Wahid Endra Joelianto Bentang Arief Budiman Doctoral Program in Engineering Physics Institut Teknologi Bandung Bandung Indonesia Mechatronics and Artificial Intelligence Universitas Pendidikan Indonesia Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia Faculty of Mechanical and Aerospace Engineering Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

Electric vehicles (EV s) boast zero tailpipe emissions, directly improving air quality and lowering greenhouse gas emissions. Despite the promise of EVs for sustainable transportation, their limited range is a key challenge. Regenerative braking is a promising approach for enhancing the driving range. This research proposes a study of Interval Type-2 Fuzzy Logic (IT2FL) control to improve regenerative braking efficiency in an electric trike. Employing IT2FL control for regenerative braking recovers 35.84% of energy, translating to a 91.053 km increase in driving range. The IT2FL method achieved a range of 50 km/kWh, representing a significant 41.7% improvement. Furthermore, utilizing the Nie-Tan Method reducer within the IT2FL framework could extend the range by an additional 2.3 km, resulting in a 3 % increase in energy recovery. These findings demonstrate the effectiveness of IT2FL control in enhancing regenerative braking efficiency for the electric trike.

关键词： Fuzzy logic Electric potential Transportation Electric vehicles Air quality Informatics Greenhouse gases

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Enhancing Fault Diagnosis Accuracy in Electric Motors: A Digital Twin Approach with Transformer Model

Enhancing Fault Diagnosis Accuracy in Electric Motors: A Dig...

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Asian Control Conference

作者： Imron Rosyadi Yul Yunazwin Nazaruddin Parsaulian Ishaya Siregar Doctoral Program of Engineering Physics Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Electrical Engineering Department Universitas Jenderal Soedirman Purwokerto Indonesia Department of Engineering Physics Instrumentation and Control Research Group Institut Teknologi Bandung Bandung Indonesia National Center for Sustainable Transporation Technology CRCS Building 2nd Floor Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper explores the integration of Digital Twin (DT) technology and machine learning (ML) techniques for predictive maintenance in electric vehicles (EVs), particularly focusing on fault diagnosis in electric motors (EMs). DT technology, which creates a digital replica of a physical entity, combined with real-time sensor data analytics, offers significant potential for improving the reliability and operational efficiency of EMs under variable and intense conditions. By incorporating advanced ML models like Transformers, which are adept at capturing long-range dependencies, into the DT framework, the study addresses critical challenges in fault diagnosis under non-stationary conditions typical in EVs. The research introduces a novel approach that combines Transformers with convolutional layers to enhance the fault diagnosis capabilities by effectively capturing complex patterns in time-series data. Furthermore, incremental learning is proposed within the DT framework to adapt to evolving environments and maintain high diagnostic performance, aiming for domain invariance in fault diagnosis models. The paper evaluates various deep learning models, including Transformers, FCNs, CNNs, Inception, ResNet, and MLP, across metrics such as accuracy, training duration, and inference time, identifying CNNs and FCNs as optimal due to their balance between performance and processing time. This study not only demonstrates the feasibility of advanced ML techniques in enhancing DT-based fault diagnosis but also provides insights into selecting appropriate models for predictive maintenance applications in EVs, highlighting the potential for future research in adapting these technologies to different datasets and operational scenarios.

关键词： Fault diagnosis Deep learning Training Adaptation models Accuracy Transformers Digital twins

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Performance Analysis of Received Signal Level for HSR using Optimized Path Loss Model

Performance Analysis of Received Signal Level for HSR using ...

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IEEE International Conference on Research, Innovation and Vision for the Future

作者： Selvi Lukman Yul Yunazwin Nazaruddin Endra Joelianto Bo Ai Doctoral Program of Engineering Physics Faculty of Industrial Technoloy Institut Teknologi Bandung Bandung Indonesia Departement of Engineering Physics Instrumentation Control Research Group Institut Teknologi Bandung National Center for Sustainable Transportation Technology Bandung Indonesia State Key Laboratory of Rail Traffic Control Safety Beijing Jiaotong University Beijing P.R. China

ISBN: (纸本)9781665461672

A reliable transmission link for High-speed rail scenarios (HSR) requires denser networks as the unique characteristics of HSR propagation emerged. In this study, we present a performance analysis of the Received Signal Level (RSL) for HSR in the free space environment as the most favorable propagation. To achieve an excellent reception, the study of path loss from the implementation of optimization technique is utilized as the most important property in the analysis. The findings demonstrate a satisfying RSL result that was achieved from the optimized path loss values. Hence it will benefit the HSR network designers to obtain a reliable transmission link in their communication's domain.

关键词： Analytical models Receivers Reliability engineering Propagation losses Rail transportation Communications technology Performance analysis

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Transparent, low-autofluorescence microECoG device for simultaneous Ca2+ imaging and cortical electrophysiology in vivo

Transparent, low-autofluorescence microECoG device for simul...

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作者： Zátonyi, A. Madarász, M. Szabó, O. Lőrincz, T. Hodován, R. Rózsa, B. Fekete, Z. Research Group for Implantable Microsystems Faculty of Information Technology and Bionics Pázmány Péter Catholic University Budapest Hungary Microsystems Laboratory Institute for Technical Physics and Material Sciences Centre for Energy Research HAS Budapest Hungary Doctoral School of Chemical Engineering and Material Sciences University of Pannonia Veszprém Hungary Laboratory of 3D Functional Network and Dendritic Imaging Institute of Experimental Medicine HAS Budapest Hungary János Szentágothai PhD Program Semmelweis University Budapest Hungary Roska Tamás Doctoral School of Sciences and Technology Pázmány Péter Catholic University Budapest Hungary

Objective. Multimodal neuroimaging approaches are beneficial to discover brain functionalities at high spatial and temporal resolution. In our work, a novel material composition of a microECoG device relying on Parylene HT and indium-tin-oxide (ITO) is presented, which facilitates two-photon imaging of Ca2+ signals and concurrent recording of cortical EEG. Approach. Long-term stability of the interfaces of the transparent microdevice is confirmed in vitro by electrochemical and mechanical tests. The outstanding optical properties, like high transmittance and low auto-fluorescent are proven by fluorimetric measurements. Spatial resolution of fluorescent two-photon imaging through the microECoG device is presented in transgenic hippocampal slices, while concurrent recording of Ca2+ signals and cortical EEG is demonstrated in vivo. Photoartefacts and photodegradation of the materials are also investigated in detail to provide safety guidelines for further use in two-photon in vivo imaging schemes. Main results. Two-photon imaging of Ca signals can be safely performed through the proposed transparent ECoG device, without significant distortion in the dimensions of detected neuronal structures or in the temporal signaling. In chronic use, we demonstrated that fluorescent Ca signals of individual neurons can be clearly recorded even after 51 d. Significance. Our results give a firm indication that highly transparent microECoG electrode arrays made of Parylene HT/ITO/Parylene HT multilayer are excellent candidates for synergetic recording of optical signals and EEG from intact brains with high resolution and are free of electrical and optical artefacts. © 2020 IOP Publishing Ltd.

关键词： Electrophysiology

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