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13th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2023

作者： Sudimanto Trisetyarso, Agung Kartowisastro, Iman Herwidiana Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department Binus Graduate Program Computer Science Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta11480 Indonesia

ISBN: (纸本)9798350323184

Electrodermal activity (EDA) is a general term for all electrical phenomena occurring on the skin, both passive and active. EDA measurements are used by researchers to measure levels of stress, emotion, mental strain, and so on. Measuring human stress levels, emotions, and mental strain are generally associated with the skin conductance response. The function GSR sensor is not only used to read people's psychology but also can be used as a pain sensor used to read the degree of pain in the skin. This pilot study uses sample data from ***. The *** data is galvanic skin response sensor data. The output of this sensor is the conductivity value that occurs in the skin. The data obtained from *** will be extracted using the mean, standard deviation, maximum, minimum, RMS, skewness, and peak-to-peak characteristics. The extracted functions are selected using the forward selection method. The results of the feature selection are three features with an accuracy percentage greater than 50%, namely the mean feature, the RMS feature, and the skewness feature. The machine learning models used are bagged tree, SVM, and K-NN models. Of the three models used, the bagged tree model has the highest accuracy rate, at 98.05%, with an F1 score is 0.9807. The KNN model with k=10 has the lowest level of accuracy compared to other models, at 96.75%. © 2023 IEEE.

关键词： Extraction

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Output Tracking Control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

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IEEE Access 2024年 12卷 18520-18535页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Skruch, Pawel University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2M9 Canada Shiraz University of Technology Electrical Engineering Faculty Shiraz71557-13876 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan AGH University of Science and Technology Department of Automatic Control and Robotics Kraków30-059 Poland

This paper introduces an innovative optimal control approach to achieve output tracking while incorporating H2-performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy model (TSFM). The primary innovation lies in extending partial eigenstructure assignment to TSFM-based nonlinear systems within the framework of sliding mode control (SMC). We propose a two-step methodology for designing optimal sliding surface gains. Initially, optimal state feedback gains are computed for each rule consequence containing a linear subsystem, adhering to predetermined eigenvalues and satisfying H2-performance criteria. Subsequently, using a convex combination, the overall state feedback gain is calculated and utilized to design sliding matrix gains. The sliding matrix gains are then determined by strategically combining previously calculated state-feedback gains in a convex optimization problem. We reframe the output tracking strategy as a stabilization problem using a virtual control input and reformulate the optimization task concerning tracking state errors. This process yields state feedback gains, sliding gains, and the formulation of the virtual control input. The effectiveness of our approach is verified through comprehensive simulations, emphasizing its capability in addressing output tracking challenges within nonlinear systems. © 2013 IEEE.

关键词： Transient analysis

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Design and Development of an Internet of Things (IoT)-Based Application and System to Detect Stunting in Infants and Toddlers

Design and Development of an Internet of Things (IoT)-Based ...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Theresa Tiffany James Jeremia Zener Sukra Lie Automotive and Robotics Engineering Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350380439

ISBN: (纸本)9798350380446

Currently, the process of monitoring children’s growth in Indonesia relies on manual methods for collecting anthropometric data. These methods pose a risk of data recording errors. Additionally, the ratio of healthcare workers to children in Indonesia is 1:90, a significant disparity that hinders efforts to reduce the prevalence of stunting in the country. To support the third Sustainable Development Goal (SDG), which emphasizes “Good Health and Well-being,” it is crucial to address issues in the stunting management process, from data collection to monitoring. The design and development of this iOS-based mobile application aim to assist in monitoring child growth and provide guidance and steps for prevention and treatment. Besides application development, it also involves the design and development of an Internet of Things (IoT) system to aid in the collection of children’s anthropometric data. The research methodology includes studying stunting cases in Indonesia, anthropometric data calculation methods, stunting prevention and management steps, application design and development, automatic anthropometric data collection, and online data storage and transmission using IoT technology. The developed application and system have been tested and successfully integrated. The IoT device has an accuracy rate of 99.83% for weight measurement calibration and 98.68% for height or length measurement calibration. From the testing results of the child condition calculation method in the application, an accuracy rate of 100% was achieved.

关键词： Weight measurement Pediatrics Accuracy Prevention and mitigation Data collection Mobile applications Calibration Internet of Things Monitoring Testing

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Nonlinear Model Predictive Control for Optimal Dose Administration in Radiotherapy 15th

Nonlinear Model Predictive Control for Optimal Dose Administ...

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15th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2024

作者： Araújo, João C. G. Guerreiro, Bruno J. Oliveira, Luis B. da Silva, Filipe Ferreira CTS-UNINOVA and LASI Department of Electrical and Computer Engineering NOVA School of Science and Technology Caparica2829-516 Portugal Institute for Systems and Robotics LARSyS Lisbon1049-001 Portugal CEFITEC Department of Physics NOVA School of Science and Technology Caparica2829-516 Portugal

ISBN: (纸本)9783031638503

This work discusses the nonlinear modeling of healthy and cancer cell population growth under the influence of radiotherapy, and proposes predictive control techniques to determine the optimal dose for tumor control, while sparing surrounding healthy tissue. Radiation effects are modelled by a linear-quadratic formalism, coupled with a novel population-specific delayed dose response. Numerical simulations of the open- and closed-loop system are conducted, and robustness against model uncertainty is studied. Results illustrate the controller’s performance when dealing with imperfect information and unexpected system behavior, and how it can be tuned to produce the desired treatment outcome. However, optimal operation is conditioned by a limited prediction horizon, as well as strict adherence to treatment schedules and dose restrictions. © IFIP International Federation for Information Processing 2024.

关键词： Nonlinear systems

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A Modified Feature Selection Algorithm for Intrusion Detection System Based on Student Psychology-Based Optimization with Explainable AI

A Modified Feature Selection Algorithm for Intrusion Detecti...

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2023 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2023

作者： Evan, Md. Raisul Islam Motiur Rahman, Mohammad Tanzir Mehedi, Sk. Nazib, Abdullah Islam, Rafiqul Rahman, Ziaur Mawlana Bhashani Science and Technology University Computer Science and Engineering Tangail1902 Bangladesh Mawlana Bhashani Science and Technology University Information and Communication Technology Tangail1902 Bangladesh School of Electrical Engineering and Robotics Queensland University of Technology BrisbaneQLD Australia School of Computing Mathematics and Engineering Charles Sturt University Bathurst Australia School of Computer Science Queensland University of Technology BrisbaneQLD Australia

ISBN: (纸本)9798350341072

Intrusion Detection Systems (IDS) play a vital role in safeguarding network security by monitoring and identifying both normal and intrusive activities. However, constructing an effective IDS from extensive datasets poses a significant challenge. The process of feature selection from these datasets is crucial, as it not only enhances accuracy but also reduces the time required to build the model. In this paper, we employ a modified version of the Student Psychology-Based Optimization (SPBO) algorithm to identify the most relevant features set for IDS. Following feature selection, various machine learning algorithms are employed for multi-level classification. To evaluate our approach, we utilize the NLS-KDD dataset. Our proposed algorithm surpasses several state-of-the-art feature selection methods in terms of accuracy, precision, recall, Fl-score, and finally faster convergence. Specif-ically, our proposed bio-inspired SPBO algorithm combined with the Random Forest (RF) algorithm achieves a comparable accuracy rate of 99.81 %. To ensure better comprehensibility, we employ two widely used Explainable Artificial Intelligence (XAI) techniques: SHapley Additive exPlanations (SHAP) and Local In-terpretable Model-Agnostic Explanations (LIME). These methods provide insightful explanations for our model's decisions, further enhancing its interpretability. © 2023 IEEE.

关键词： Feature Selection

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Traffic Signs Detection System Using YOLO (You Only Look Once) That Provides Notification

Traffic Signs Detection System Using YOLO (You Only Look Onc...

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Smart Mechatronics (ICSMech), IEEE International Conference on

作者： Yosua Setiawan Muhammad Nurul Puji Winda Astuti Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350368918

ISBN: (纸本)9798350368925

Road accidents have become a problem for many road users in many countries. It may affect human daily activity and tasks, potentially influencing productivity and safety. Therefore, reducing road accidents as swiftly and effectively as possible is critical. For this reason, a Traffic signs detection system has been developed in this work to improve road safety. The detection system can detect traffic signs on the road such as traffic lights, speed limits, and no U-turn signs, and then it will provide users with the corresponding text on an LCD and vibration feedback using a motor. Throughout this work, the You Only Look Once (YOLO) algorithm is proposed for detecting traffic signs on the road. Moreover, a camera is used to extract live videos which serve as an input for the Raspberry Pi 4 model B containing custom YOLOv5 and YOLOv8 models. The system then sends the output to an LCD, a vibration motor, and a radio frequency (RF) module as notifications for users. In the end, the performance of the proposed method has been collected and it is found to produce good accuracy within an inconsiderable amount of 1.677 images and 2.263 annotations. Therefore, the accuracy of 0.69 for YOLOv5 and 0.7 for YOLOv8 were obtained from the training and validation process. Furthermore, the system accurately detects the traffic signs on the road.

关键词： YOLO Vibrations Training Road accidents Accuracy Motors Liquid crystal displays Road safety Safety Videos

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Happy: A Debiased Learning Framework for Continual Generalized Category Discovery 38

Happy: A Debiased Learning Framework for Continual Generaliz...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ma, Shijie Zhu, Fei Zhong, Zhun Liu, Wenzhuo Zhang, Xu-Yao Liu, Cheng-Lin MAIS Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics HKISI-CAS China School of Computer Science and Information Engineering Hefei University of Technology China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Constantly discovering novel concepts is crucial in evolving environments. This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD), which aims to incrementally discover new classes from unlabeled data while maintaining the ability to recognize previously learned classes. Although several settings are proposed to study the C-GCD task, they have limitations that do not reflect real-world scenarios. We thus study a more practical C-GCD setting, which includes more new classes to be discovered over a longer period, without storing samples of past classes. In C-GCD, the model is initially trained on labeled data of known classes, followed by multiple incremental stages where the model is fed with unlabeled data containing both old and new classes. The core challenge involves two conflicting objectives: discover new classes and prevent forgetting old ones. We delve into the conflicts and identify that models are susceptible to prediction bias and hardness bias. To address these issues, we introduce a debiased learning framework, namely Happy, characterized by Hardness-aware prototype sampling and soft entropy regularization. For the prediction bias, we first introduce clustering-guided initialization to provide robust features. In addition, we propose soft entropy regularization to assign appropriate probabilities to new classes, which can significantly enhance the clustering performance of new classes. For the harness bias, we present the hardness-aware prototype sampling, which can effectively reduce the forgetting issue for previously seen classes, especially for difficult classes. Experimental results demonstrate our method proficiently manages the conflicts of C-GCD and achieves remarkable performance across various datasets, e.g., 7.5% overall gains on ImageNet-100. Our code is publicly available at https://***/mashijie1028/Happy-CGCD. © 2024 Neural information processing systems foundation. All rights reserved.

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REMOTELY CONTROLLABLE DNA MICROFLOW 26

REMOTELY CONTROLLABLE DNA MICROFLOW

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26th International Conference on Miniaturized Systems for Chemistry and Life sciences, MicroTAS 2022

作者： Udono, Hirotake Nomura, Shin-Ichiro M. Takinoue, Masahiro Department of Computer Science School of Computing Tokyo Institute of Technology Japan Department of Robotics Graduate School of Engineering Tohoku University Japan

ISBN: (纸本)9781733419048

Despite an emerging interest in fluid manipulation within confined environment such as biological cells and micro-scale biomimics, remotely controllable micro-scale flow remains unexplored. However, intracellular microfluidics is not compatible with established top-down microfluidic methods because of limited accessibility. Here, we demonstrate photoresponsive DNA fluid, condensate of DNA nanostructures (DNA motifs), as a versatile mechanistic tool for the remote control of microflow. Insertion site of azobenzene, introduced for photocontrollability, determines an extent of dynamic shift in binding enthalpy between DNA motifs, providing the underpinning mechanism for drastic and facile flow-mode switching. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life sciences. All rights reserved.

关键词： Remote control

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Stress detection system for social media users 7

Stress detection system for social media users

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Febriansyah, Mochamad Rizky Nicholas Yunanda, Rezki Suhartono, Derwin Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Software Engineering Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automated mental health assessments in previous studies. Additionally, the widespread usage of social media has served as a potential data source for developing such models. Our research tried to detect whether the users of social media were under stress or not. We used a dataset from Dreaddit consisting of posts from one of the popular social media platforms, Reddit. We propose a machine learning model consisting of Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Bag of Words, and Term Frequency - inverse document frequency (TF-IDF) for stress detection. The final evaluation of the model achieved an 80.00% F-1 Score and 75.00% accuracy, and both were scored by SVM. © 2022 Elsevier B.V.. All rights reserved.

关键词： Support vector machines

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A Multi-trace Surface Integral Equation Solver to Analyze Electromagnetic Field Interactions on Graphene-based Devices

A Multi-trace Surface Integral Equation Solver to Analyze El...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2022

作者： Zhao, Ran Chen, Liang Hu, Jun Bagci, Hakan Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division Thuwal23955 Saudi Arabia School of Electronic Science and Engineering Chengdu610056 China

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

A multi-trace surface integral equation (MT-SIE) solver is proposed to analyze electromagnetic field interactions on graphene-based devices. This solver decomposes the computation domain into an exterior subdomain (the background medium where the device resides in) and an interior subdomain that represents the device's dielectric substrate. The infinitesimally thin graphene sheet partially covers the interface between these two subdomains. On the graphene surface and the rest of this interface, resistive Robin transmission conditions (formulated for the first time in this work) and the traditional Robin transmission conditions are used to "connect"these two subdomains, respectively. In each subdomain, the electric and magnetic field equations are used as the governing equations. Various numerical examples are presented to demonstrate the accuracy and the applicability of the proposed MT-SIE solver. © 2022 IEEE.

关键词： Graphene

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