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arXiv 2023年

作者： Angelopoulos, Anastasios N. Duchi, John C. Zrnic, Tijana Department of Electrical Engineering and Computer Sciences UC Berkeley United States Departments of Statistics and Electrical Engineering Stanford University United States Department of Statistics and Stanford Data Science Stanford University United States

We present PPI++: a computationally lightweight methodology for estimation and inference based on a small labeled dataset and a typically much larger dataset of machine-learning predictions. The methods automatically adapt to the quality of available predictions, yielding easy-to-compute confidence sets—for parameters of any dimensionality—that always improve on classical intervals using only the labeled data. PPI++ builds on prediction-powered inference (PPI), which targets the same problem setting, improving its computational and statistical efficiency. Real and synthetic experiments demonstrate the benefits of the proposed adaptations. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Use and usability of software verification methods to detect behaviour interference when teaching an assistive home companion robot: A proof-of-concept study

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Paladyn 2021年第1期12卷 402-422页

作者： Koay, Kheng Lee Webster, Matt Dixon, Clare Gainer, Paul Syrdal, Dag Fisher, Michael Dautenhahn, Kerstin Department of Computer Science School of Physics Engineering and Computer Science University of Hertfordshire College Lane Hertfordshire HatfieldAL10 9AB United Kingdom School of Computer Science and Mathematics Liverpool John Moores University LiverpoolL2 2QP United Kingdom Department of Computer Science University of Manchester ManchesterM13 9PL United Kingdom Department of Computer Science University of Liverpool Liverpool United Kingdom Department of Computer Science School of Physics Engineering and Computer Science University of Hertfordshire College Lane Hertfordshire HatfieldAL10 9AB United Kingdom Departments of Electrical and Computer Engineering and Systems Design Engineering University of Waterloo ON Canada

When studying the use of assistive robots in home environments, and especially how such robots can be personalised to meet the needs of the resident, key concerns are issues related to behaviour verification, behaviour interference and safety. Here, personalisation refers to the teaching of new robot behaviours by both technical and non-technical end users. In this article, we consider the issue of behaviour interference caused by situations where newly taught robot behaviours may affect or be affected by existing behaviours and thus, those behaviours will not or might not ever be executed. We focus in particular on how such situations can be detected and presented to the user. We describe the human-robot behaviour teaching system that we developed as well as the formal behaviour checking methods used. The online use of behaviour checking is demonstrated, based on static analysis of behaviours during the operation of the robot, and evaluated in a user study. We conducted a proof-of-concept human-robot interaction study with an autonomous, multi-purpose robot operating within a smart home environment. Twenty participants individually taught the robot behaviours according to instructions they were given, some of which caused interference with other behaviours. A mechanism for detecting behaviour interference provided feedback to participants and suggestions on how to resolve those conflicts. We assessed the participants' views on detected interference as reported by the behaviour teaching system. Results indicate that interference warnings given to participants during teaching provoked an understanding of the issue. We did not find a significant influence of participants' technical background. These results highlight a promising path towards verification and validation of assistive home companion robots that allow end-user personalisation. © 2021 Kheng Lee Koay et al., published by De Gruyter.

关键词： Human robot interaction

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Integrated Path Planning and Tracking Control of Marine Current Turbine in Uncertain ocean Environments

Integrated Path Planning and Tracking Control of Marine Curr...

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2022 American Control Conference, ACC 2022

作者： Hasankhani, Arezoo Ondes, Tugrul Baris Tang, Yufei Sultan, Cornel Van Zwieten, James Florida Atlantic University Department of Computer & Electrical Engineering and Computer Science Boca RatonFL33431 United States Department of Aerospace and Ocean Engineering Virginia Tech 460 Old Turner St. BlacksburgVA24061 United States Department of Civil Environmental and Geomatics Engineering Florida Atlantic University Boca RatonFL33431 United States

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

This paper presents an integrated path planning and tracking control of marine hydrokinetic energy harvesting devices. To address the highly nonlinear and uncertain oceanic environment, the path planner is designed based on a reinforcement learning (RL) approach by fully exploring the historical ocean current profiles. The planner will search for a path to optimize a chosen cost criterion, such as maximizing the total harvested energy for a given time. Model predictive control (MPC) is then utilized to design the tracking control for the optimal path command from the planner subject to problem constraints. The planner and the tracking control are accommodated in an integrated framework to optimize these two parts in a real-time manner. The proposed approach is validated on a marine current turbine (MCT) that executes vertical waypoint path searching to maximize the net power due to spatiotemporal uncertainties in the ocean environment, as well as the path following via an MPC tracking controller to navigate the MCT to the optimal path. Results demonstrate that the path planning increases harvested power compared to the baseline (i.e., maintaining MCT at an equilibrium depth), and the tracking controller can successfully follow the reference path under different shear profiles. © 2022 American Automatic Control Council.

关键词： Model predictive control

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Deployment of the Pseudorandom Encoded light for Evaluating Biomass (PEEB) sensor in an Integrated Multi-Trophic Aquaculture (IMTA) System

Deployment of the Pseudorandom Encoded light for Evaluating ...

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Big Data V: Learning, Analytics, and Applications 2023

作者： Fairman, William Wills, Paul S. Hanisak, Dennis Karim, Md Arshadul Singh, Shagundeep Ouyang, Bing Department of Computer and Electrical Engineering and Computer Science Florida Atlantic University 777 Glades Road Boca RatonFL33431 United States Harbor Branch Oceanographic Institute Florida Atlantic University 5600 US1 North Fort PierceFL33496 United States Department of Ocean and Mechanical Engineering Florida Atlantic University 777 Glades Road Boca RatonFL33431 United States

ISBN: (数字)9781510661592

ISBN: (纸本)9781510661585

For the last decade, Harbor Branch oceanographic Institute at Florida Atlantic University (HBOI) has been developing Integrated Multi-Trophic Aquaculture (IMTA), where multiple species are farmed together. Compared with the traditional Recirculating aquaculture systems (RAS), the IMTA system can improve efficiency, reduce waste, and provide ecosystem services. For the IMTA system to be successful at a commercial farm scale, HBOI is developing an AI-centric Internet of Things framework to support the operations of the IMTA system. The Pseudorandom Encoded Light for Evaluating Biomass (PEEB) sensor is an endeavor in this effort to realize automated monitoring of the growth of the Sea Lettuce (Ulva lactuca), an important organism in the HBOI IMTA system. PEEB utilizes the measurements from a sequence of encoded light flashes to quantify the seaweed biomass. Such a configuration ensures the sensor can operate under different ambient light conditions and biomass densities. An improved PEEB sensor based on a unified electronic sensor design that is more robust against ambient conditions and capable of long-range data transmission is discussed. This electronic design will be the backbone to support future sensors for the IMTA system. Multiple PEEB sensors have been deployed at the HBOI IMTA system. The cloud-based storage and analysis of the sensor data are discussed. © 2023 SPIE.

关键词： Biomass

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Grasshopper Optimization Algorithm Based Spam Detection System Using Multi-Objective Wrapper Feature Selection and Neural Network Classification 2nd

Grasshopper Optimization Algorithm Based Spam Detection Syst...

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2nd International Conference on Emerging Technologies and Intelligent Systems, ICETIS 2022

作者： Ghaleb, Sanaa A. A. Mohamad, Mumtazimah Ghanem, Waheed A. H. M. Abdullahi, Akibu Mahmoud Nasser, Abdullah B. Saleh, Sami Abdulla Mohsen Arshad, Humaira Omolara, Abiodun Esther Abiodun, Oludare Isaac Ghetas, Mohamed Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia Faculty of Education Saber University of Lahej Lahej Yemen Faculty of Engineering University of Aden Aden Yemen Faculty of Education Aden University of Aden Aden Yemen Faculty of Computing and Informatics Albukhary International University Kedah Malaysia School of Technology and Innovation University of Vaasa Vaasa Finland School of Electrical and Electronic Engineering Universiti Sains Malaysia Nilai Malaysia Department of Computer Science Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Abuja Abuja Nigeria Department of Computer Science Bingham University Nasarawa Nigeria Faculty of Computer Science Nahda University New Beni Seuf City Egypt

ISBN: (纸本)9783031252730

Email classification is essential to the trouble of email and pattern recognition. Nowadays, a number of unsolicited messages are circulated over the internet. While plenty of machine learning techniques are a success in detecting textual primarily based on totally unsolicited mail, this isn’t the case for messages spams, which can without difficulty avoid those textual-unsolicited mail detection systems. This paper proposes the introduction of MOBGOA constructed on the binary version of the GOA algorithm, for multi-objective selection of features purposes and the wrapper approach of selection of features and purpose of the EGOAMLP algorithm utilized this algorithm as the wrapper classifier. Spam assassin dataset was used to validate the performance of the MOB-EGOAMLP. The result uncovered that the new technique outflanks the wide selection of different methods in previously published works. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature Selection

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Multimodal Deep Learning to Differentiate Tumor Recurrence from Treatment Effect in Human Glioblastoma

Multimodal Deep Learning to Differentiate Tumor Recurrence f...

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IEEE International Symposium on Biomedical Imaging

作者： Tonmoy Hossain Zoraiz Qureshi Nivetha Jayakumar Thomas Eluvathingal Muttikkal Sohil Patel David Schiff Miaomiao Zhang Bijoy Kundu Departments of Computer Science University of Virginia USA Radiology and Medical Imaging University of Virginia USA Neurology University of Virginia USA Electrical and Computer Engineering University of Virginia USA Biomedical Engineering University of Virginia USA

Differentiating tumor progression (TP) or recurrence from treatment-related necrosis (TN) is critical for clinical management decisions in glioblastoma (GBM). Dynamic FDG PET (dPET), an advance from traditional static FDG PET, may prove advantageous in clinical staging. dPET includes novel methods of a model-corrected blood input function that accounts for partial volume averaging to compute parametric maps that reveal kinetic information. In a preliminary study, a convolution neural network (CNN) was trained to predict classification accuracy between TP and TN for 35 brain tumors from 26 subjects in the PET-MR image space. 3D parametric PET Ki (from dPET), traditional static PET standardized uptake values (SUV), and also the brain tumor MR voxels formed the input for the CNN. The average test accuracy across all leave-one-out cross-validation iterations adjusting for class weights was 0.56 using only the MR, 0.65 using only the SUV, and 0.71 using only the Ki voxels. Combining SUV and MR voxels increased the test accuracy to 0.62. On the other hand, MR and Ki voxels increased the test accuracy to 0.74. Thus, dPET features alone or with MR features in deep learning models would enhance prediction accuracy in differentiating TP vs TN in GBM.

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Label Noise Robustness of Conformal Prediction

arXiv

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arXiv 2022年

作者： Einbinder, Bat-Sheva Feldman, Shai Bates, Stephen Angelopoulos, Anastasios N. Gendler, Asaf Romano, Yaniv Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Israel Department of Computer Science Technion - Israel Institute of Technology Israel Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Department of Electrical Engineering and Computer Science University of California Berkeley United States Departments of Electrical and Computer Engineering and of Computer Science Technion—Israel Institute of Technology Israel

We study the robustness of conformal prediction, a powerful tool for uncertainty quantification, to label noise. Our analysis tackles both regression and classification problems, characterizing when and how it is possible to construct uncertainty sets that correctly cover the unobserved noiseless ground truth labels. We further extend our theory and formulate the requirements for correctly controlling a general loss function, such as the false negative proportion, with noisy labels. Our theory and experiments suggest that conformal prediction and risk-controlling techniques with noisy labels attain conservative risk over the clean ground truth labels whenever the noise is dispersive and increases variability. In other adversarial cases, we can also correct for noise of bounded size in the conformal prediction algorithm in order to ensure achieving the correct risk of the ground truth labels without score or data regularity. Copyright © 2022, The Authors. All rights reserved.

关键词： Prediction models

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Reinforcement Learning for Underwater Spatiotemporal Path Planning, with Application to an Autonomous Marine Current Turbine

Reinforcement Learning for Underwater Spatiotemporal Path Pl...

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American Control Conference (ACC)

作者： Arezoo Hasankhani Yufei Tang James VanZwieten Department of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton FL USA Department of Ocean and Mechanical Engineering Florida Atlantic University Boca Raton FL USA

This paper presents a reinforcement learning (RL) framework applied for an autonomous underwater vehicle (AUV) path planning, focusing on a specific type of energyharvesting AUV, entitled marine current turbine (MCT). The proposed RL-based approach improves a classical path planning to adopt with an underwater environment prone to spatiotemporal uncertainties. The path planning problem is formulated to achieve the goal of maximizing the harnessed energy from the MCT subject to the agent dynamics and the spatiotemporal environment constraints. Three RL algorithms, including Q-learning, deep Q-network (DQN), and proximal policy optimization (PPO), are nominated to deal with the path planning over both discrete gridded and continuous underwater environments modeling. The experimental results demonstrate the efficiency of the RL-based approaches in seeking the optimal path in the underwater environment, where further discussion is presented to generalize the proposed approach to other energy-harvesting autonomous vehicles operating in the spatiotemporally varying environment, such as airborne wind turbines.

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Aerosol jetted PZT actuated fiber MEMS scanner for potential microscopic imaging

Aerosol jetted PZT actuated fiber MEMS scanner for potential...

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Nano-, Bio-, Info-Tech Sensors, and Wearable Systems 2022

作者： Wang, Wei-Chih Yen, Colin Peng, Kuang-Chen Hsu, Yi-Feng Wu, Wen-Jong Department of Mechanical Engineering University of Washington SeattleWA98185 United States Department of Electrical and Computer Engineering University of Washington SeattleWA98185 United States Institute of Nanoengineering and Microsystems National Tsinghua University Hsinchu300 Taiwan Power Mechanical Engineering National Tsinghua University Hsinchu300 Taiwan Department of Engineering Science and Ocean Engineering National Taiwan University Taiwan

ISBN: (数字)9781510649668

ISBN: (纸本)9781510649651

In the past few years, the demand for various types of medical endoscopes such as of gastroscopy, laryngoscopy, and bronchoscopy has been increasing significantly. The advancement of technology has not only created a demand for more accurate and precise medical endoscopes but also created a demand for a more compact and miniaturized medical endoscopes, which could ultimately reduce pain and discomfort for patients or even prevent perforation and infections in the worst-case scenarios. In this study, a Micro-Electro-Mechanical System (MEMS) optical scanner is presented as a new type of medical endoscope. This optical scanner device makes use of a lead-zirconate-titanate piezoelectric (PZT) ceramic film and a chemically etched tapered optical fiber to create a push-pull actuator. The push-pull actuator is then excited to drive the optical fiber as a detector to receive optical signal. The scanner device is fabricated by using photolithography processes to define patterns on our stainless-steel sheets, which will act as the substrate, and Aerosol Deposition (AD) method to deposit thin PZT films onto our stainless-steel sheets. Even though the scanner device is in its early stages of development, we were able to achieve various scanner patterns and motions by the proposed push-pull actuator. ANSYS finite element method was also used to not only provide a fair comparison to the practical results, but also used to design the required resonant frequency that can excite the push-pull actuator. © 2022 SPIE

关键词： Scanners Ferroelectric materials Microelectromechanical systems Actuators Optical fibers Aerosols Optical scanning Mirrors Spatial resolution Waveguides

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Fishing net underwater localization using acoustic signals 31st

Fishing net underwater localization using acoustic signals

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31st International ocean and Polar engineering Conference, ISOPE 2021

作者： Park, Jeongyong Jin, Chungkuk Kwon, Do-Soo Kim, Moohyun Kim, Kiseon Department of Ocean Engineering Texas A&M University College StationTX United States School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Korea Republic of

ISBN: (纸本)9781880653821

A fishing net localization algorithm based on underwater acoustic sensor networks is proposed. Fishing vessels are utilized as mobile references for underwater localization. After collecting acoustic signals, the algorithm determines the underwater location of fishing nets, and different estimation methods are developed based on the number of ships detecting the signal. It then tracks the location in real-time to better determine whether the net is lost or discarded so that it can be collected in a timely manner. The algorithm is tested on various system parameters such as an acoustic signal range, the number of ships, and current velocity to verify its robustness. © 2021 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Ships

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