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Cooperative Multilift Slung Load Transportation Using Haptic Admittance Control Guidance

Journal of Guidance, Control, and Dynamics 2022年第10期45卷 1899-1912页

作者： Romano, Matthew Ye, Anne Pye, John Atkins, Ella University of Michigan Ann ArborMI48109 United States Robotics Institute. Student Member AIAA United States Undergraduate Student Computer Science United States Undergraduate Student Aerospace Engineering United States Aerospace Engineering and Robotics. Fellow AIAA United States

This paper presents the first haptically guided cooperative small unmanned aircraft system payload transport system with experimental validation. A team of five quadrotors is connected with individual tethers to an instrumented payload. A user issues haptic guidance commands by applying force on the slung payload. The system estimates user-applied force and direction from inline tension sensors in real time. This force updates the virtual dynamics of an admittance controller to execute the desired trajectory of the payload in the direction of the applied force. A centralized payload controller commands quadrotor trajectories to track the admittance controller trajectory. Custom open-source autopilot software tracks user commands by translating each quadrotor to pull on the tethers in a manner that collectively moves the payload according to user input force. Simulation and experimental flight-test results in a netted facility validate the system with a user applying force directly on the slung payload to guide it. © 2022 by the authors.

关键词： Slings

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Machine learning based cardiovascular disease prediction

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Materials Today: Proceedings 2022年 64卷 459-463页

作者： Chinnasamy, P. Arun Kumar, S. Navya, V. Lakshmi Priya, K. Sruthi Boddu, Siva Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering Bethesda Institute of Technology and Science Gwalior India Department of Electronics and Communication Engineering East Point College of Engineering & Technology Bangalore India Department of Electronics and Communication Engineering PERI Institute of Technology Chennai India UG Student Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

In past few years, one of the really tough situations in medicine is already making predictions cardiac dysfunction. Almost any moment, roughly one patient dies through myocardial infarction with in present era. Due to the difficulty of forecasting cardiac sickness, it is vital to systematize the procedure in place to minimize the risk associated with and to educate the patient well enough in preparation. The risk of cardiovascular disease in the World is tremendous. The accurate and thorough assessment of a physician's cardiovascular risk is critical for reducing the incidence and severity attack and stroke and improving cardiovascular protection. In order to solve this issue, we are proposing method to discover these heart abnormalities as soon as reasonably practicable to avert fatal consequences. The next step is to determine whether or not the user is at risk of developing cardiovascular disease. It also answers difficult problems concerning heart detection of diseases, allowing clinicians to make better clinical decisions. Medical specialists have accumulated a large number of medical data that can be analysed and important fact retrieved. Machine learning approaches can improve in the prognosis and early identification of heart illness after hypothesis testing. © 2022

关键词： Machine learning

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Necessity of Self-explainability in AI: Insights from the Existing Social Projects

Necessity of Self-explainability in AI: Insights from the Ex...

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International Conference on Next Generation Systems and Networks, BITS EEE CON 2022

作者： Jadhav, Mayur K. Laddha, Shilpa S. Kabra, Sanchit M. Ph.D. Student Department of Computer Science Engineering Government College of Engineering Maharashtra Chh. Sambhajinagar India Head of Department Information Technology Government College of Engineering Maharashtra Chh. Sambhajinagar India Computer Science Engineering BITs Pilani India

ISBN: (纸本)9789819904822

Self-explanatory artificial intelligence has lately gained prominence as researchers and practitioners strive to make their methodologies more understandable. It is not contentious to assert that researching how individuals explain to one another may be a useful beginning point for explanation in artificial intelligence, given that the majority of this research has focused on publicly explaining decisions or actions to a human observer. However, it is reasonable to say that the vast bulk of self-explanatory artificial intelligence research is based solely on the researchers’ subjective opinions of what constitutes a "good" explanation. Extensive and essential study in science and technology has been conducted on the definition, creation, selection, assessment, and presentation of explanations by humans. This study contends that people use certain cognitive biases and social expectations in the explanation process. This paper covers relevant studies from several fields and makes the case that self-explainable artificial intelligence should expand on some current research. It highlights several key results and explores how they may be combined with research on self-explainable artificial intelligence. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Explainability Explanation Interpretability Self-éxplainable AI Transparency

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A Comprehensive Study of Plant Disease Detection Using Deep Learning Methods 1st

A Comprehensive Study of Plant Disease Detection Using Deep ...

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1st International Conference on Advanced Communication and Intelligent Systems, ICACIS 2022

作者： Haider, Kashan Prasandeep Ahmed, Maaz Pal, Ankit Rawat, Sur Singh Gupta, Vimal Shaw, Rabindra Nath Ghosh, Ankush Student Department of Computer Science and Engineering JSS Academy of Technical Education Noida201301 India Faculty Department of Computer Science and Engineering JSS Academy of Technical Education Noida201301 India University Center for Research and Development Chandigarh University Punjab India

ISBN: (纸本)9783031250873

Growing crops for human consumption and financial benefit is known as agriculture. It is the capability of using cutting-edge methods to cultivate crops and raise animals. As a result, the quality of the crop production is crucial to the development of the economy. In the agriculture industry, plant diseases and pests pose the biggest threat to crop production. Thus, it is essential to develop a method for accurately recognizing these illnesses so that the appropriate steps can be taken to treat them, increasing crop quality and quantity. A reliable disease detection system can be created by combining tools for classifying and extracting information with computer vision or image processing techniques employing deep learning. Different researchers have offered different approaches for identifying plant diseases. This study analyses these techniques. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cultivation

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UAV weaknesses against deauthentication based hijacking attacks 22

UAV weaknesses against deauthentication based hijacking atta...

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2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics, SAMI 2024

作者： Krasnyanszki, Bruno Brassai, Sandor Tihamer Nemeth, Andras Óbuda University Under Graduate Student in Computer Science Engineering Budapest Hungary Sapientia Hungarian University of Transylvania Department of Electrical Engineering Corunca Romania National University of Public Service Department of Electronic Warfare Budapest Hungary

ISBN: (纸本)9798350317206

Unmanned Aerial Vehicles (UAVs) and Unmanned Aerial Systems (UAS) are more important than ever before and we're using it daily for agriculture, photo making, film industry, Search & Rescue, long range cable quality control, Geo Informatics, cartography, and state use (including border protection, disaster recovery and the military). Most of the UAV/UAS devices are using digital communication protocols nowadays, especially the IEEE 802.11 Wireless Local Area of Network for the cheaper and middle price UAV/UAS. Unfortunately, the protocols specified by the previously mentioned IEEE standard are vulnerable against some attacks. This research paper proposes a method that can interrupt the normal operation of the UAV/UAS that are using the IEEE 802.11 protocol and hijack the interrupted UAV/UAS permanently. For this method, deauthentication attack has been performed against the UAV/UASs. The attack vector is capable of denying the communication between the UAV and the control station. With sufficient prior knowledge it is possible to hijack the UAV that communicates on a 802.11 based protocol. Deauthentication based attacks are much more sophisticated than for example traditional electronic jamming, because the target can be precisely chosen and the attack will not interrupt other devices using the same communication method. The research has unfortunately some limitations in equipment, measurements conducted only in three different types of UAVs from three price categories, but in the future the authors would like to extend the range of UAVs. From this three UAVs two are hijackable by this method and the third should also be hijackable with some modifications. This research could be a base later for UAV/UAS counter measure systems for the safety of all of us. © 2024 IEEE.

关键词： Drones

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Towards Optimized Physical Activity: A Fusion of Graph Convolutional Networks and XGBoost for Exercise Analysis

Towards Optimized Physical Activity: A Fusion of Graph Convo...

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Smart Systems for Electrical, Electronics, Communication and computer engineering (ICSSEECC), International Conference on

作者： Kishan V Rajesh M Dhanush B Rajiv Vincent School of Computer Science and Engineering Student Vellore Institute of Technology Chennai Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India School of Computer Science and Engineering Student Vellore Institute of Technology Chennai Chennai India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

This research explores the implementation of the GCN-XGBoost with attention mechanism within the detection and correction of exercises through computer vision, utilizing the 2-dimensional pose coordinates from the MMFit [ 1 ] dataset. The objective is to enhance the precision and reliability of squat performance analysis, leveraging the robust, scalable machine learning capabilities of XGBoost. By processing and analyzing two-dimensional skeletal data, the study aims to identify key postural deviations and provide corrective feedback in realtime, potentially reducing the risk of injury and improving overall exercise effectiveness. Initial results demonstrate the model's capability to discern between correct and incorrect exercise forms with high accuracy, indicating the feasibility of applying advanced machine learning techniques to personal fitness and rehabilitation contexts. This paper details the methodology, from data preprocessing to model training and evaluation, highlighting the contributions to the fields of sports science and machine learning in physical activity monitoring. We have used MMFit dataset for the model that we have trained. We use the 2D coordinates of 18 of the 33 joints in the human body as the main features to categorize various exercises and forecast the accuracy of the poses identified by the Google's AI framework MediaPipe [2]. In this paper, we first classify an individual's exercises (squats, lunges, bicep curls, situps, pushups, triceps extensions, dumbbell rows, jumping jacks, dumbbell shoulder press, lateral shoulder raises) and check whether the pose associated with an exercise is correct. The final model trained has a 98% final validation accuracy.

关键词： Training Presses Accuracy Lung Internet Performance analysis Reliability

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Asteroid Hazard Prediction Using Machine Learning: A Comparative Analysis of Different Algorithms 3

Asteroid Hazard Prediction Using Machine Learning: A Compara...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Sharma, Tushar Sharma, Shamneesh Sharma, Ajay Kumar, Aman Malik, Arun Sharma, Abhimanyu Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India UpGrad Education Private Limited Delivery and Student Success upGrad Campus India

ISBN: (纸本)9798350330724

The prediction of asteroid risk is a crucial topic in the realm of space technology. The utilization of machine learning techniques has enabled the identification of asteroids in photographic and video data obtained from telescopes and satellites, prediction of their trajectory, simulation of their potential impact on Earth, assessment of the probability and magnitude of such an impact, and provision of support to decision-makers in the event of an impact. The implementation of these techniques can enhance our comprehension and preparedness for prospective asteroid collisions, along with our capacity to effectively rank initiatives and distribute resources targeted at mitigating the gravity of such an impact. Furthermore, the utilization of these techniques can enhance our readiness for prospective collisions with asteroids. The present study introduces a novel model for predicting asteroid hazards, which incorporates multiple variables such as asteroid dimensions, speed, path, and atmospheric conditions. The employed methodology involves the utilization of machine learning algorithms to scrutinize past records of asteroid collisions and replicate plausible impact scenarios. The training process involves utilizing 70% of the available data for training the model, while the remaining 30% is reserved for testing purposes. By implementing these methods in current research, researchers have achieved a noteworthy accuracy of 99.99% in the task of classifying asteroids. The model is subjected to comparative analysis with alternative methods, and it exhibits superior precision in comparison to the latter. © 2023 IEEE.

关键词： Forecasting

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Clustering of Tidal Swamp Soil Based on NPK Macronutrients using the FCM Method 4

Clustering of Tidal Swamp Soil Based on NPK Macronutrients u...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Suryati Ermatita Rini, Dian Palupi Budianta, Dedik University of Sriwijaya Doctoral Student of Engineering Science Faculty of Engineering Palembang Indonesia Indo Global Mandiri University Computer Science Faculty Palembang Indonesia Sriwijaya University Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9781665473279

Tidal swamp resistance is divided into several types, namely type A, B, C and type D. This is divided based on the type of seawater overflow, with the presence of several types, each type has different characteristics, this study aims to enrich the soil, by clustering tidal swamp land using the variables sodium, phosphorus and Potassium (NPK). NPK is a macro nutrient that is needed by plants to grow and develop plants planted on the soil, considering that tidal swamp land is not fertile soil. This research was conducted in the village of Mulia Sari, Tanjung Lago District, Banyuasin Regency with type A. In solving this soil clustering problem, the researcher chose the FCM (Fuzzy C-Miens Clustering) method, because this method is included in the unsupervised learning technique. This method is used to find patterns in a tidal soil dataset. FCM is a popular fuzzy algorithm and can achieve a convergent cluster center. The initial step taken in this study was to measure the NPK macro elements contained in the soil dataset, by taking soil samples to be examined in the soil laboratory, determining the number of clusters, the study determined 3 (three) clusters based on the NPK variable. Then carry out the data processing using the FCM method. From the data processing, the sodium cluster was found in the 15th iteration, while the phosphorus cluster was found in the 17th iteration and Potassium was found in the 16th iteration. © 2022 IEEE.

关键词： Soils

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Classification of Usual and Atypical Ductal Hyperplasia using Deep Learning

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Procedia computer science 2025年 260卷 561-569页

作者： Puneet Pandit Shrinivas D Desai Gururaj N Bhadri Student School of Computer Science & Engineering KLE Technological University Hubli - 580031 Professor School of Computer Science & Engineering KLE Technological University Hubli – 580031 Professor Department of Mathematics KLE Technological University Hubli - 580031

Breast Cancer is the leading cause of death among women. In the diagnosis of preinvasive breast cancer, some of the intraductal proliferations pose a special challenge. One such challenge includes the classification of usual ductal hyperplasia (UDH), atypical ductal hyperplasia (ADH), and ductal carcinoma in situ (DCIS). Prognosis and diagnosis of intraductal breast lesions pinpointing invasive and non-invasive cancer lesions is a monotonous task due to their similar structures in biopsy samples of breast. Expert’s differential diagnosis between breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) is of great importance in arriving at an optimal treatment plan. In this research work, we present AI based solution to classify the most confusing stages of breast cancer, that is Usual ductal hyperplasia (UDH), Atypical ductal hyperplasia (ADH) and Ductal carcinoma in situ (DCIS) in breast biopsy. Statistical analysis is carried out to justify the results. Data set having images of normal mammary glands, invasive ductal lesions, non-invasive ductal lesions which include Usual ductal hyperplasia (UDH), Atypical ductal hyperplasia (ADH), Ductal carcinoma in situ (DCIS) in breast biopsy specimens are employed in this study. Statistical analysis is performed to provide the statistical evidence of experiments. Analysis of Variance (ANOVA) as a feature selection method is used to select best features. The proposed method has performed quite significantly, where scores are statistically and medically acceptable. The highest accuracy recorded is 83.33%. When tested for robustness, the accuracy has dropped down to 65.25% allowing one to carry out further investigations.

关键词： Deep Learning Classification Intraductal lesion Invasive ductal carcinoma (IDC) Dimensionality curse Analysis of Variance (ANOVA)

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Milling Force Prediction of Titanium Alloy Based on Support Vector Machine and Ant Colony Optimization

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-12页

作者： Zhang, Xiaoxia Yu, Ziqiao Hu, Yinyin Yang, Jiao A Professor of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan11041 China A Master Student of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Milling force prediction of titanium alloy plays an important role in titanium alloy milling process. In the article, the milling process of titanium alloy materials is analyzed, and this material quality is affected directly by the milling force. Support vector machine (SVM) has shown prominent performance for many real-world problems. However, SVM computational complexity increases, as the number of samples increases. How to effectively apply it to massive datasets is still a serious challenge. Although there are some literatures on parameter optimization techniques of SVM regression, the performance of this model still needs to be further studied and improved. We present a new milling force predicting model, namely ACO&SVM, which integrate SVM regression with ACO algorithm to enhance the prediction accuracy of milling force for mill process of titanium alloy. The primary innovation feature of hybrid model is to apply the ACO to automatically determine parameters of SVM regression model. In addition, as being applied to discrete optimization, to solve continuous optimization problems, ACO algorithm needs to transform continuous variables into discrete ones by discretizing of continuous variables. The results have shown that proposed ACO&SVM model yields better prediction accuracy, and thus it can be widely applied to the fields of material processing optimization. © 2021. All Rights Reserved.

关键词： Titanium alloys

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