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10th International Conference on Applied System Innovation, ICASI 2024

作者： Loo, Fu-Nie Chen, Tung-Shou Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei National Taichung University of Science and Technology Department of Computer Science and Information Engineering No.129 Sec.3 Sanmin Rd. North Dist. Taichung City404 Taiwan National Chung Hsing University Department of Computer Science and Engineering 145 Xingda Rd. South Dist. Taichung City402202 Taiwan China Medical University Master Program for Digital Health Innovation No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung City406040 Taiwan

ISBN: (纸本)9798350394924

Industrial Control systems (ICS) automate industrial processes but also introduces cybersecurity threats. Intrusion Detection System (IDS) are crucial for detecting cyber-attacks on ICS, yet zero-day attacks are often inefficiently detecting with supervised learning. This study employs semi-supervised learning using one-class SVM, isolation forest, and Local Outlier Factor (LOF), to train IDS models. Utilizing dataset collected from a self-build virtual ICS environment, the study demonstrates the feasibility of these models in detecting common attack like Injection, ARP, and Man-in-the-Middle. © 2024 IEEE.

关键词： Zero-day attack

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An Optimal Framework for SDN Based on Deep Neural Network

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computers, Materials & Continua 2022年第10期73卷 1125-1140页

作者： Abdallah Abdallah Mohamad Khairi Ishak Nor Samsiah Sani Imran Khan Fahad RAlbogamy Hirofumi Amano Samih M.Mostafa Department of Industrial Engineering School of Applied Technical Sciences German Jordanian UniversityAmman35247Jordan School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyThe National University of Malaysia(UKM)Bangi43600SelangorMalaysia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan Turabah University College Computer Sciences ProgramTaif UniversityTaif21944Saudi Arabia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network ***,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in *** address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is *** approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection *** initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on *** assaults were found when suspected irregular traffic was *** reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy *** false alarm rate(FAR)is much lower than that of the information entropy-based detection ***,the proposed framework can shorten the detection time and improve the resource utilization efficiency.

关键词： Deep neural network computer networks data security optimization

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ViTacTip: Design and Verification of a Novel Biomimetic Physical Vision-Tactile Fusion Sensor

ViTacTip: Design and Verification of a Novel Biomimetic Phys...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Wen Fan Haoran Li Weiyong Si Shan Luo Nathan Lepora Dandan Zhang Department of Bioengineering Imperial College London School of Engineering Mathematics and Technology and Bristol Robotics Laboratory University of Bristol School of Computer Science and Electronic Engineering University of Essex Department of Engineering King’s College London

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Tactile sensing is significant for robotics since it can obtain physical contact information during manipulation. To capture multimodal contact information within a compact framework, we designed a novel sensor called ViTacTip, which seamlessly integrates both tactile and visual perception capabilities into a single, integrated sensor unit. ViTacTip features a transparent skin to capture fine features of objects during contact, which can be known as the see-through-skin mechanism. In the meantime, the biomimetic tips embedded in ViTacTip can amplify touch motions during tactile perception. For comparative analysis, we also fabricated a ViTac sensor devoid of biomimetic tips, as well as a TacTip sensor with opaque skin. Furthermore, we develop a Generative Adversarial Network (GAN)-based approach for modality switching between different perception modes, effectively alternating the emphasis between vision and tactile perception modes. We conducted a performance evaluation of the proposed sensor across three distinct tasks: i) grating identification, ii) pose regression, iii) contact localization and force estimation. In the grating identification task, ViTacTip demonstrated an accuracy of 99.72%, surpassing TacTip, which achieved 94.60%. It also exhibited superior performance in both pose and force estimation tasks with the minimum error of 0.08 mm and 0.03N, respectively, in contrast to ViTac’s 0.12 mm and 0.15N. Results indicate that ViTacTip outperforms single-modality sensors.

关键词： Biomimetics Force Estimation Switches Robot sensing systems Skin Sensors

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Apply Masked-attention Mask Transformer to Instance Segmentation in Pathology Images 6

Apply Masked-attention Mask Transformer to Instance Segmenta...

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6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Sheng, Jia-Chun Liao, Yi-Sheng Huang, Chun-Rong National Chung Hsing University Department of Computer Science and Engineering Taichung402 Taiwan National Cheng Kung University Cross College Elite Program Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan701 Taiwan

ISBN: (纸本)9798350301953

Instance segmentation can be applied for the discrimination and diagnosis of cancer cells in pathology images. Accurate segmentation of each pathological cell in the pathology images can improve the efficiency of clinical diagnosis. In this paper, we aim to evaluate the state-of-the-art transformer-based instance segmentation method, masked-attention mask transformer (Mask2Former)[1], on pathology datasets. With the pretrained model of Mask2Former on the natural image instance segmentation dataset, we show that Mask2Former can be adaptive to small pathological datasets and achieve comparable or even better instance segmentation performance compared with the state-of-the-art task-specific pathology image instance segmentation methods. © 2023 IEEE.

关键词： Pathology

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Robust STL Control Synthesis under Maximal Disturbance Sets

Robust STL Control Synthesis under Maximal Disturbance Sets

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IEEE Conference on Decision and Control

作者： Joris Verhagen Lars Lindemann Jana Tumova Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science University of Southern California Los Angeles CA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This work addresses maximally robust control synthesis under unknown disturbances. We consider a nonlinear system, subject to a Signal Temporal Logic (STL) specification and jointly synthesize the maximal possible disturbance bounds and the corresponding controllers that ensure the STL specification is satisfied under these bounds. Many works have considered STL satisfaction under given bounded disturbances yet, to the authors’ best knowledge, this is the first work that aims to maximize the permissible disturbance set and find corresponding maximally robust controllers. We, therefore, introduce disturbance robustness as a model-based robustness metric for STL planning and control synthesis. We extend the notion of disturbance-robust semantics for STL, which is a property of a specification, dynamical system, and controller, and provide an algorithm for maximally robust controllers satisfying an STL specification using Hamilton-Jacobi reachability. We show its soundness and provide a simulation example with an Autonomous Underwater Vehicle (AUV).

关键词： Measurement Robust control Heuristic algorithms Semantics Control systems Robustness Planning Logic Dynamical systems Nonlinear systems

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Development of Detection Kidney Function Stand Alone System and Design based on Oral Ammonia Levels

Development of Detection Kidney Function Stand Alone System ...

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International Conference of computer and Informatics engineering (IC2IE)

作者： William Natasha Angeline Hansen Yosica Mariana Elioenai Sitepu Winda Astuti Melinda Siswanto Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Product Design Program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Biotechnology Engineering Department Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

The development of detection kidney function system at the first prototype have problem with the dependence on the internet, which make not practical in the development of the system and the initial design was not ergonomic. In this work the development of the second step is done with the improvement of accuracy compared with other methods. In this work the new design of the embedded system is also applied and in term of algorithm, the system applied for woman patient. Three different types of machine learning techniques, namely K-nearest neighbors (KNN), Decision Tree and Random Forest are applied and compared. The AI-based kidney failure severity identification system with KNN algorithm had an average accuracy of 90% and 91% for training and testing accuracy, respectively. The design of the embedded system also renews with stylist, simple and practical.

关键词： Training Accuracy Machine learning algorithms Embedded systems Nearest neighbor methods Vectors Decision trees Kidney Random forests Testing

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Built Different: Tactile Perception to Overcome Cross-Embodiment Capability Differences in Collaborative Manipulation

arXiv

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arXiv 2024年

作者： van den Bogert, William Iyengar, Madhavan Fazeli, Nima The Mechanical Engineering Department The University of Michigan MI United States The Computer Science Department The University of Michigan MI United States The Robotics Department The University of Michigan MI United States

Tactile sensing is a powerful means of implicit communication between a human and a robot assistant. In this paper, we investigate how tactile sensing can transcend cross-embodiment differences across robotic systems in the context of collaborative manipulation. Consider tasks such as collaborative object carrying where the human-robot interaction is force rich. Learning and executing such skills requires the robot to comply to the human and to learn behaviors at the joint-torque level. However, most robots do not offer this compliance or provide access to their joint torques. To address this challenge, we present an approach that uses tactile sensors to transfer policies from robots with these capabilities to those without. We show how our method can enable a cooperative task where a robot and human must work together to maneuver objects through space. We first demonstrate the skill on an impedance control-capable robot equipped with tactile sensing, then show the positive transfer of the tactile policy to a planar prismatic robot that is only capable of position control and does not come equipped with any sort of force/torque feedback, yet is able to comply to the human motions only using tactile feedback. Further details and videos can be found on our project website at https://***/research/tactile-collaborative/. © 2024, CC BY.

关键词： Feedback

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The effect of visual advanced driver assistance systems on a following human driver in a mixed-traffic condition

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Procedia computer science 2023年 216卷 221-229页

作者： Dedy Ariansyah Bens Pardamean Giandomenico Caruso Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy

Rapid development in vehicular technology has caused more automated vehicle control to increase on the roads. Studies showed that driving in mixed traffic with an autonomous vehicle (AV) had a negative impact on the time headway (THW) of conventional vehicles (CVs) (i.e., driven by humans). To address this issue, there is a need to equip CV with visual advanced driver assistance systems (ADASs) that helps the driver maintain safe headway when driving near AVs. This study examines the perception of drivers using visual ADAS and their associated risk while driving behind the AV at constant and varying speeds. The preliminary results showed that while visual ADAS could help drivers keep the safe THW, it could affect drivers’ ability to react to emergencies. This implies that visual modality alone might not be sufficient and therefore requires some other feedback or intelligent transport systems to help drivers maintain safe driving in a mixed-traffic condition.

关键词： sustainable transport management platooning car-following visual advanced driver assistance system virtual realtiy

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Recurrent Neural Network-based Modeling Approach for 4D Printing Design : Long sequence prediction based on short sequences

Recurrent Neural Network-based Modeling Approach for 4D Prin...

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Yifan Xu Mengtao Wang Zhongkui Wang Lin Meng Graduate School of Science and Engineering Ritsumeikan University Kusatsu Shiga Japan Department of Robotics Ritsumeikan University Kusatsu Shiga Japan Department of Electronic and Computer Engineering Ritsumeikan University Kusatsu Shiga Japan

ISBN: (数字)9798350366204

ISBN: (纸本)9798350366211

This study presents a method for predicting the deformation of hydrogel models fabricated by four-dimensional printing technology using deep learning. In this method, a large number of hydrogel models with the same shape but different distributions of length and expansion ratio are first created using the modeling software Abaqus. Then, deformation simulations are performed based on finite element analysis using the thermal expansion method, and the simulation results are processed and organized to create a data set. Afterward, a recurrent neural network (RNN) was created and trained using a composite dataset consisting of multiple hydrogel model sequences of different lengths to enable the RNN to learn model deformation features and, accordingly, predict the deformation of various longer sequences of hydrogels in a very short period of time while recording the change in accuracy of the prediction for each length. This research provides a new approach to modeling and inverse design for 4D printing technology.

关键词： Deformable models Printing Thermal expansion Recurrent neural networks Deformation Hydrogels Simulation Predictive models Software Thermal analysis

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Offline Content-based Recommendation System for Wikimedia Commons Contents 16

Offline Content-based Recommendation System for Wikimedia Co...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Maitaigahasse, Ahidjo Ebongue Fendji, Jean Louis Kedieng Atemkeng, Marcellin Department Mathematics and Computer Science Faculty of Science University of Ngaoundere Ngaoundere454 Cameroon Centre for Research Experimentation and Production School of Chemical Engineering and Mineral Industries University of Ngaoundere Ngaoundere454 Cameroon Wallenberg Research Centre Stellenbosch University Stellenbosch South Africa Department of Robotics and Industrial Informatics School of Chemical Engineering and Mineral Industries University of Ngaoundere Ngaoundere454 Cameroon Department of Mathematics Rhodes University P.O. Box 6140 Grahamstown South Africa

Recommendation systems are essential for enhancing digital experiences, but their reliance on internet connectivity limits accessibility in regions with limited or no access. This paper presents an offline content-based recommendation system designed to operate without internet dependency by leveraging precomputed Term Frequency-Inverse Document Frequency (TF-IDF) vectors and cosine similarity. To optimize offline performance, we store a locally computed TF-IDF matrix, allowing efficient retrieval of relevant media items through matrix multiplication instead of real-time computation. The system is evaluated using twenty simulated personas representing diverse user interests, demonstrating its ability to generate personalized relevant recommendations. By eliminating the need for online data access, our system makes educational content from Wikimedia Commons accessible in remote areas, schools, and offline learning environments. These findings highlight the potential of offline recommendation systems in bridging the digital divide and providing equitable access to personalized learning resources. © 2025 Elsevier B.V.. All rights reserved.

关键词： Interactive computer systems

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