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1st International Conference on Cognitive Computation and systems, ICCCS 2022

作者： An, Zhaohui Min, Gaochen Jiang, Xiangzuo Yu, Xinbo He, Wei Silvestre, Carlos School of Intelligence Science and Technology Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing100083 China Donald Bren School of Information and Computer Sciences University of California IrvineCA92697 United States Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China LARSyS Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

ISBN: (纸本)9789819927883

In this paper, an adaptive radial basis function neural network (RBFNN) control strategy is proposed for bilateral teleoperation robotic system with uncertainty and time delay. Meantime, the force observer method is used to estimate the interaction force between the slave robot and environment. The model of teleoperation robotic system is analyzed, and then the uncertainty of bilateral teleoperation robotic system is estimated by using RBFNN. Using Lyapunov stability theorem, the stability of teleoperation robotic system under our proposed control is proved. Finally, the effectiveness of the proposed control algorithm is verified by Matlab Simulink, and position tracking and force estimation performance of the teleoperation robotic system are guaranteed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time delay

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Next generation Fruit Vending Machine using Artificial Intelligence 6

Next generation Fruit Vending Machine using Artificial Intel...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Sivasubramanian, S. Kalyana Sundaram, N. Padhi, S.N. Uike, Dipesh Uma Maheswari, B. Banupriya, V. New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Tamil Nadu Chennai600073 India Koneru Lakshmaiah Education Foundation Department of Mechanical Engineering Andhra Pradesh Vaddeswaram522302 India Dr. Ambedkar Institute of Management Studies and Research Masters in Business Administration Maharashtra Nagpur440010 India St. Joseph's College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai600119 India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Tamil Nadu Karur639113 India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

An automatic vending machine is designed to supply people with a variety of items, such as snacks, beverages, newspapers, and tickets without any human intervention. According to the money that is deposited into a vending machine as well as the product that has been selected by the user, the machine will determine the item and will distribute it to the user. In the proposed work, the vending machine has been designed to distribute fruits to the user as per their requirement. Classification algorithms have been used to predict the type of fruits required by the user with the help of the input provided by camera. The load cell is used to measure the kilogram or the quantity of the fruits as per the requirement by using some input peripherals like keyboard. The proposed system is also a user interactive based once. Here, there is a display device that has interfaced with the system and the display device will provide information such as the fruit which has been chosen and the quantity of the fruit that the user has entered and also shares the information on the status of the requirements. So, it will be useful for the user to know the process going in the vending machine. The raspberry pi microprocessor has employed here as a processor along the required input and output peripherals like LCD, Keypad, Load cell, camera, and motors. The machine learning algorithm like a support vector machine has been employed to predict the type of fruit as per the requirements of the user. The insertion of intelligence like machine learning algorithms in the vending machine is comparatively providing better performance. The long-term objective is to equip a vending machine solution that is both affordable and efficient, therefore boosting the shopping experience of customers and increasing the need for widespread deployment of intelligence in smart vending machines. © 2022 IEEE.

关键词： Support vector machines

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Fuzzy Control of a Hydrodynamic Process

Fuzzy Control of a Hydrodynamic Process

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Igor Krčmar Petar Marić Velibor Đalić Aleksandar Rakić Bojan Derajić Faculty of Electrical Engineering University of Banja Luka Banja Luka BiH The Department of Signals and Systems School of Electrical Engineering University of Belgrade Belgrade Serbia Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany

Hydrodynamic processes, part of many industrial and urban infrastructure systems, usually lack accurate models, hence introducing ambiguity in a controller design procedure. Operators and control engineers provide significant knowledge about successful control of hydrodynamic systems. Fuzzy controller, suitable to exploit and incorporate available knowledge, is designed for control of a benchmark coupled tank system. Successful fuzzy controller must cope with slow dynamics of the process, dead zone of an actuator, and unipolar control signal, when employed in reference tracking tasks. The designed fuzzy controller implements fuzzy inference system with 15 rules, two inputs and one output, as well as three adjustable gain parameters. The proposed controller is successful in reference tracking tasks and disturbance suppression. Experiments, organised as reference tracking tasks and implemented as computer simulations, support the analysis.

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A Framework for Hyperspectral Image Classification

A Framework for Hyperspectral Image Classification

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Augmented Intelligence and Sustainable systems (ICAISS), International Conference on

作者： Phaneendra Kumar B L N Radhesyam Vaddi Prabukumar Manoharan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India Department of Inforamtion Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

The high volume and replication of data in hyperspectral images results in improper classification. This paper addresses a non-linear spectral feature extraction using Local Linear Embeddings in which graph-based structures are utilized. The representative features are then trained for smoothed features using a spatial filter. The extracted spatial features are then trained for classification using a nonlinear version of Support Vector Machine. The proposed model is tested on two datasets: Indian Pines & Pavia. Proposed approach is tested with well-known dimensionality reduction techniques. The reported Overall Accuracy is 92.33% & 98.21% on Indian Pines & Pavia respectively and outperformed well. The algorithm is used for hyperspectral change detection.

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Streaming quantum state purification for general mixed states

arXiv

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arXiv 2025年

作者： Grier, Daniel Leung, Debbie Li, Zhi Pashayan, Hakop Schaeffer, Luke Department of Mathematics Department of Computer Science and Engineering University of California San Diego United States Institute for Quantum Computing University of Waterloo Canada Perimeter Institute for Theoretical Physics Canada National Research Council of Canada Canada Dahlem Center for Complex Quantum Systems Freie Universität Berlin Germany Research Institute Taiwan Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland United States

Given multiple copies of a mixed quantum state with an unknown, nondegenerate principal eigenspace, quantum state purification is the task of recovering a quantum state that is closer to the principal eigenstate. A streaming protocol relying on recursive swap tests has been proposed and analysed for noisy depolarized states with arbitrary dimension and noise level. Here, we show that the same algorithm applies much more broadly, enabling the purification of arbitrary mixed states with a nondegenerate principal eigenvalue. We demonstrate this through two approaches. In the first approach, we show that, given the largest two eigenvalues, the depolarized noise is the most difficult noise to purify for the recursive swap tests, thus the desirable bounds on performance and cost follow from prior work. In the second approach, we provide a new and direct analysis for the performance of purification using recursive swap tests for the more general noise. We also derive simple lower bounds on the sample complexity, showing that the recursive swap test algorithm attains optimal sample complexity (up to a constant factor) in the low-noise regime. © 2025, CC BY.

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Online Covariance Matrix Estimation in Sketched Newton Methods

arXiv

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arXiv 2025年

作者： Kuang, Wei Anitescu, Mihai Na, Sen Department of Statistics The University of Chicago United States Mathematics and Computer Science Division Argonne National Laboratory United States School of Industrial and Systems Engineering Georgia Institute of Technology United States

Given the ubiquity of streaming data, online algorithms have been widely used for parameter estimation, with second-order methods particularly standing out for their efficiency and robustness. In this paper, we study an online sketched Newton method that leverages a randomized sketching technique to perform an approximate Newton step in each iteration, thereby eliminating the computational bottleneck of second-order methods. While existing studies have established the asymptotic normality of sketched Newton methods, a consistent estimator of the limiting covariance matrix remains an open problem. We propose a fully online covariance matrix estimator that is constructed entirely from the Newton iterates and requires no matrix factorization. Compared to covariance estimators for first-order online methods, our estimator for second-order methods is batch-free. We establish the consistency and convergence rate of our estimator, and coupled with asymptotic normality results, we can then perform online statistical inference for the model parameters based on sketched Newton methods. We also discuss the extension of our estimator to constrained problems, and demonstrate its superior performance on regression problems as well as benchmark problems in the CUTEst set. © 2025, CC BY.

关键词： Newton-Raphson method

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Chatbot and its Impact on the Retail Industry

Chatbot and its Impact on the Retail Industry

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Reshmi Tatikonda M. Kempanna Ramya Thatikonda A. Bhuvanesh Ragupathi Thota R. Keerthanadevi Department of Computer Information Systems University of the Central Missouri Warrensburg USA Department of AI and ML Bangalore Institute of Technology Bengaluru India Department of Information Technology University of the Cumberlands Williamsburg USA Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India Department of Information Technology Jawaharlal Technological University Hyderbad India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This study explores the transformative potential of chatbots in the retail industry, highlighting their role in enhancing customer engagement, optimizing business processes, and revolutionizing the nature of retail interactions. Through a comprehensive review of existing literature and case studies, the paper delves into the cognitive processes underlying human-chatbot interactions and examines their impact on customer experience, satisfaction, and loyalty. Chatbots serve a wide array of operational functions, including inventory management, automated order processing, data-driven analytics, and employee training. These functionalities streamline workflows, improve efficiency, and maximize resource utilization. The paper further explores the innovative applications of chatbots, including AI-driven personalization, conversational commerce, and seamless integration with emerging technologies. By analyzing both the current applications and challenges of chatbot technology in retail, the study uncovers the exciting opportunities they present for shaping the future of retail by fostering more personalized, efficient, and interactive customer experiences.

关键词： Industries Training Reviews Psychology Transforms Inventory management Chatbots Multilingual Resource management Sustainable development

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Objective diagnosis of ADHD through movement analysis by using a smart chair with piezoelectric material

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Pediatrics and Neonatology 2023年第1期64卷 46-52页

作者： Chang, Tung-Ming Wu, Rong-Ching Yang, Rei-Cheng Chiang, Ching-Tai Chiu, Yi-Hung Ouyang, Chen-Sen Wang, Yun-Ming Wu, Ming-Hsu Lin, Guang-Chung Lin, Lung-Chang Department of Pediatric Neurology Changhua Christian Children's Hospital Changhua Taiwan Department of Electrical Engineering I-Shou University Taiwan Departments of Pediatrics Kaohsiung Medical University Hospital Kaohsiung Medical University Taiwan Department of Computer and Communication National Pingtung University Taiwan Department of Information Engineering I-Shou University Taiwan Department of Biological Science and Technology Institute of Molecular Medicine and Bioengineering Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B) National Yang Ming Chiao Tung University Taiwan Department of Pediatrics School of Medicine College of Medicine Kaohsiung Medical University Taiwan Department of Biological Science and Technology College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan

Background: Attention-deficit/hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in schoolchildren. ADHD diagnoses are generally made based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The diagnosis is made clinically based on observation and information provided by parents and teachers, which is highly subjective and can lead to disparate results. Considering that hyperactivity is one of the main symptoms of ADHD, the inaccuracy of ADHD diagnosis based on subjective criteria necessitates the identification of a method to objectively diagnose ADHD. Methods: In this study, a medical chair containing a piezoelectric material was applied to objectively analyze movements of patients with ADHD, which were compared with those of patients without ADHD. This study enrolled 62 patients—31 patients with ADHD and 31 patients without ADHD. During the clinical evaluation, participants’ movements were recorded by the piezoelectric material for analysis. The variance, zero-crossing rate, and high energy rate of movements were subsequently analyzed. Results: The results revealed that the variance, zero-crossing rate, and high energy rate were significantly higher in patients with ADHD than in those without ADHD. Classification performance was excellent in both groups, with the area under the curve as high as 98.00%. Conclusion: Our findings suggest that the use of a smart chair equipped with piezoelectric material is an objective and potentially useful method for supporting the diagnosis of ADHD. © 2022 Taiwan Pediatric Association

关键词： Attention-deficit/hyperactivity disorder piezoelectric material smart chair

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MR2CPPIS: Accurate prediction of protein–protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism

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computers in Biology and Medicine 2024年 176卷 108543-108543页

作者： Gong, Yinyin Li, Rui Liu, Yan Wang, Jilong Cao, Buwen Fu, Xiangzheng Li, Renfa Chen, Danny Z. College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Hunan University Changsha410082 China Peng Cheng Laboratory Shenzhen518066 China College of Information and Electronic Engineering Hunan City University Yiyang413002 China Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States

Proteins play a vital role in various biological processes and achieve their functions through protein–protein interactions (PPIs). Thus, accurate identification of PPI sites is essential. Traditional biological methods for identifying PPIs are costly, labor-intensive, and time-consuming. The development of computational prediction methods for PPI sites offers promising alternatives. Most known deep learning (DL) methods employ layer-wise multi-scale CNNs to extract features from protein sequences. But, these methods usually neglect the spatial positions and hierarchical information embedded within protein sequences, which are actually crucial for PPI site prediction. In this paper, we propose MR2CPPIS, a novel sequence-based DL model that utilizes the multi-scale Res2Net with coordinate attention mechanism to exploit multi-scale features and enhance PPI site prediction capability. We leverage the multi-scale Res2Net to expand the receptive field for each network layer, thus capturing multi-scale information of protein sequences at a granular level. To further explore the local contextual features of each target residue, we employ a coordinate attention block to characterize the precise spatial position information, enabling the network to effectively extract long-range dependencies. We evaluate our MR2CPPIS on three public benchmark datasets (Dset 72, Dset 186, and PDBset 164), achieving state-of-the-art performance. The source codes are available at https://***/YyinGong/MR2CPPIS. © 2024 Elsevier Ltd

关键词： Forecasting

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Toward VR in VR: Assessing Engagement and Social Interaction in a Virtual Conference

Toward VR in VR: Assessing Engagement and Social Interaction...

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作者： Moreira, Catarina Simoes, Francisco P. M. Lee, Mark J. W. Zorzal, Ezequiel R. Lindeman, Robert W. Pereira, Joao Madeiras Johnsen, Kyle Jorge, Joaquim Queensland University of Technology School of Information Systems BrisbaneQLD4000 Australia Universidade Federal Rural de Pernambuco Departamento de Computação Recife52171-900 Brazil La Trobe University Computer Science and Information Technology BundooraVIC3086 Australia University of Canterbury HIT Lab NZ Christchurch8041 New Zealand Instituto Superior Técnico Universidade de Lisboa INESC-ID Department of Computer Science and Engineering Lisboa1000-029 Portugal University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States

The pandemic brought about an unprecedented number of virtual conferences, given the heavy restrictions on travel to in-person meetings. Despite all the advances in technology, people still complain about virtual events. There is Zoom fatigue, confinement malaise, and a longing for personal social interactions. This paper discusses our experience organizing the IEEE Virtual Reality Conference (IEEE VR) as a virtual event. IEEE VR was a success with 1200+ registered paying participants, dozens of workshops and tutorials, and hundreds of technical papers. We used (1) a virtual environment platform, together with (2) discussion tools and (3) videoconferencing/broadcast/online tools to further provide effective social interaction and increase engagement. In this paper, we explore the synergies between virtual environments and other online tools and assess user engagement by analyzing the messages exchanged between participants across different genders and geographical regions. To this end, we apply diverse engagement metrics for online conferences. Our analysis shows that these metrics have the potential to highlight engagement, diversity, and inclusion by combining textual messages, participant geographic and gender information, communities of participants, and visitation patterns in a virtual environment. Drawing on our results and experiences, we propose guidelines for organizing technical virtual events to increase diversity and social interaction. © 2013 IEEE.

关键词： Virtual reality

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