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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： darko Radakovic Anuradha Singh Aparna S. Varde Pankaj Lal Environmental Science and Management Ph.D. Program Montclair State University NJ USA Clean Energy and Sustainability Analytics Center (CESAC) Montclair State University NJ USA Department of Computer Science Montclair State University NJ USA

Pillars of smart cities include smart environment, mobility and economy. We explore impacts on these to enhance smart cities, heading towards a smart planet. Our motivation emerges from the need to decarbonize transportation. In this context, ride-sharing companies deploy electric vehicles (EVs). These should be managed by various factors: battery demand, EV charging station location, service availability, and charging time. Ride-sharing EV s aim to maximize profits via more rides. Our paper explores game theory in AI here. We propose E-Ride-Minimax, adapting the Minimax algorithm, treating EV ride-sharing companies as players. We hypothesize one player choosing its next move via total passenger-travel distance (longer the distance, larger the profit); and another player via battery usage (ratio of total passenger-travel distance to vehicle-passenger distance: optimizing this ratio enables more travel without recharging). Experimental results reveal that rising passenger numbers yield maximum battery savings (e.g. rush hours / major events); followed by stable and falling numbers. Our findings indicate that E-Ride-Minimax can reduce battery usage in some circumstances by 64%, losing 1 % profit. This is vital, given global emphasis on climate change. It increases cost-effectiveness, consumer participation and passengers per mile; reduces energy use and greenhouse gas emissions; and thus helps decarbonize transportation.

关键词： Climate change Smart cities Artificial intelligence Economics Smart devices Environmental factors Shared transport

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Psychological Stress Classification Using Extreme Gradient Boosting Algorithm

Psychological Stress Classification Using Extreme Gradient B...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Maria Mahardini Sakanti Viacheslav Siniaev Aurelia Amaris Win-Jet Luo Suhartono C. Bambang dwi Kuncoro Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung Taichung Taiwan Institute of Applied Computer Science ITMO University St. Petersburg Russian Federation Department of Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang Indonesia Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taichung Taiwan Department of Refrigeration Air Conditioning and Energy Engineering National Chin-Yi University of Technology Taichung Taiwan

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAd dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers.

关键词： Accuracy Microcontrollers Psychology Human factors Predictive models Boosting Prediction algorithms Real-time systems Sensors Wearable devices

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Hybrid Stochastic Synapses Enabled by Scaled Ferroelectric Field-effect Transistors

arXiv

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arXiv 2022年

作者： Islam, A.N.M. Nafiul Saha, Arnob Jiang, Zhouhang Ni, Kai Sengupta, Abhronil School of Electrical Engineering & Computer Science The Pennsylvania State University University ParkPA16802 United States Microsystems Engineering Ph.D. Program Rochester Institute of Technology RochesterNY14623 United States

Achieving brain-like density and performance in neuromorphic computers necessitates scaling down the size of nanodevices emulating neuro-synaptic functionalities. However, scaling nanodevices results in reduction of programming resolution and emergence of stochastic non-idealities. While prior work has mainly focused on binary transitions, in this work we leverage the stochastic switching of a three-state ferroelectric field effect transistor (FeFET) to implement a long-term and short-term 2-tier stochastic synaptic memory with a single device. Experimental measurements are performed on a scaled 28nm high-k metal gate technology-based device to develop a probabilistic model of the hybrid stochastic synapse. In addition to the advantage of ultra-low programming energies afforded by scaling, our hardware-algorithm co-design analysis reveals the efficacy of the 2-tier memory in comparison to binary stochastic synapses in on-chip learning tasks - paving the way for algorithms exploiting multi-state devices with probabilistic transitions beyond deterministic ones. © 2022, CC BY.

关键词： Stochastic models

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The Indoor People Tracking and Counting System

The Indoor People Tracking and Counting System

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International Automatic Control Conference

作者： Hao-Pu Lin Chin-Chuan Han Jun-Wei Liao Chong-Han Ciou Yi-Cheng Hong Ming-Lun Tan Ph. D. Program in Material and Chemical Engineering National United University MiaoLi Taiwan Department of Computer Science and Information Engineering National United University MiaoLi Taiwan

COVId-19 outbreaks and becomes serious from 2019 winter. Taking body temperature, wearing masks, recording footprint and avoiding crowds become important tasks and require a lot of manpower support for the prevention of the epidemic in every. In this study, we have integrated the infrared thermometer devices to measure people temperature and the people identities through RGB images at the entrance of buildings. Using the computer vision and deep learning methods, we would like to build a 24/7 and region-wide coverage assistance system to automatically record the footprint of a specific people in the building. We also detect if people taking off their masks in the yard at any time. The processing videos are grabbed from the existing CCTV systems. The developed system also locates the crowd and calculate the number of people in a specified area, and the manager can know the number of people and control the access in real time. It will reduce the burden of manpower. In addition, we have optimized the system performance to handle multiple camera videos and to reduce the hardware cost at the same time.

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Surface defect identification using Bayesian filtering on a 3d mesh

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Measurement: Sensors 2025年 38卷

作者： dalle Vedove, Matteo Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, daniele Department of Industrial Engineering Universitá di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Universitá di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

This paper presents a CAd-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAd model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAd model into a high-density polygonal mesh, where each vertex represents a state variable in 3d space. Subsequently, a weighted least squares algorithm is employed to iteratively estimate the state of the scanned workpiece based on the captured point cloud measurements. This framework offers the potential to incorporate information from diverse sensors into the CAd domain, facilitating a more comprehensive analysis. Preliminary results demonstrate promising performance, with the algorithm achieving convergence to a sub-millimeter standard deviation in the region of interest using only approximately 50 point cloud samples. This highlights the potential of utilising commercially available stereo cameras for high-precision quality control applications. © 2025

关键词： Mesh generation

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Structure-Guided discovery of Pd-1/Pd-L1 Interaction Inhibitors: Peptide design, Screening, and Optimization via Computation-Aided phage display Engineering

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Journal of Chemical Information and Modeling 2024年第5期64卷 1615-1627页

作者： Tseng, Tien-Sheng Lee, Chao-Chang Chen, Po-Juei Lin, Chiu-Yuen Chen, Wang-Chuan Lee, Yu-Ching Lin, Jiun-Han Chen, Kaun-Wen Tsai, Keng-Chang Institute of Molecular Biology National Chung Hsing University Taichung40202 Taiwan National Research Institute of Chinese Medicine Ministry of Health and Welfare Taipei11221 Taiwan The School of Chinese Medicine for Post Baccalaureate I-Shou University Kaohsiung824005 Taiwan Department of Chinese Medicine E-Da Hospital Kaohsiung824005 Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei11031 Taiwan Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei11031 Taiwan Department of Industrial Technology Ministry of Economic Affairs Taipei100210 Taiwan Food Industry Research and Development Institute Hsinchu30062 Taiwan Molecular Science and Digital Innovation Center Genetics Generation Advancement Corporation Taipei11949 Taiwan

Cancer immunotherapy harnesses the immune system to combat tumors and has emerged as a major cancer treatment modality. The Pd-1/Pd-L1 immune checkpoint modulates interactions between tumor cells and T cells and has been extensively targeted in cancer immunotherapy. However, the monoclonal antibodies known to target this immune checkpoint have considerable side effects, and novel Pd-1/Pd-L1 inhibitors are therefore required. Herein, a peptide inhibitor to disrupt Pd-1/Pd-L1 interactions was designed through structure-driven phage display engineering coupled to computational modification and optimization. BetaPb, a novel peptide library constructed by using the known structure of Pd-1/Pd-L, was used to develop inhibitors against the immune checkpoint, and specific peptides with high affinity toward Pd-1 were screened through enzyme-linked immunosorbent assays, homogeneous time-resolved fluorescence, and biolayer interferometry. A potential inhibitor, B8, was preliminarily screened through biopanning. The binding affinity of B8 toward Pd-1 was confirmed through computation-aided optimization. Assessment of B8 variants (B8.1, B8.2, B8.3, B8.4, and B8.5) demonstrated their attenuation of Pd-1/Pd-L1 interactions. B8.4 exhibited the strongest attenuation efficiency at a half-maximal effective concentration of 0.1 μM and the strongest binding affinity to Pd-1 (equilibrium dissociation constant = 0.1 μM). B8.4 outperformed the known Pd-1/Pd-L1 interaction inhibitor PL120131 in disrupting Pd-1/Pd-L1 interactions, revealing that B8.4 has remarkable potential for modification to yield an antitumor agent. This study provides valuable information for the future development of peptide-based drugs, therapeutics, and immunotherapies for cancer. © 2024 American Chemical Society.

关键词： Peptides

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Quasi-Nondestructive Read Out of Ferroelectric Capacitor Polarization by Exploiting a 2TnC Cell to Relax the Endurance Requirement

TechRxiv

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TechRxiv 2023年

作者： Xiao, Yi deng, Shan Zhao, Zijian Faris, Zubair Xu, Yixin Huang, Tzu-Jung Narayanan, Vijaykrishnan Ni, Kai Microsystems Engineering Ph.D. program Rochester Institute of Technology RochesterNY14623 United States Department of Computer Science and Engineering Penn State University University ParkPA16802 United States Department of Electrical and Microelectronic Engineering Rochester Institute of Technology RochesterNY14623 United States

In this work, we propose a 2TnC ferroelectric random access memory (FeRAM) cell design to realize the quasi-nondestructive readout (QNRO) of ferroelectric polarization (PFE) in a capacitor, which can relax the endurance requirement of the ferroelectric thin film and exploits the benefits of both FeRAM and ferroelectric FET (FeFET). We demonstrate that: i) QNRO sensing of PFE is conducted successfully in experiment with a ON/OFF ratio (ION/IOFF)>103, ION>10 µA, and read endurance>106 cycles, which can relax the FeRAM endurance requirement by 106x;ii) optimization of the cell performance can be realized by tuning the metal-ferroelectric-metal capacitor (MFM) capacitor to read transistor area ratio and read transistor threshold voltage (VTH);iii) the proposed cell structure is 3d-compatible, enabling integration of highly dense memory solution;iv) the proposed cell structure also enables compute-in-memory (CIM) applications of FeRAM, which has not been widely explored. With this technology, storage and memory-centric computing can be enabled. © 2023, CC BY.

关键词： Cytology

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Surface defect identification using Bayesian filtering on a 3d mesh

arXiv

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arXiv 2025年

作者： Vedove, Matteo dalle Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, daniele Department of Industrial Engineering Università di Trento Trento Italy DRIM Ph.D. of national interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

关键词： Mesh generation

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What affects users to click social media ads and purchase intention? The roles of advertising value, emotional appeal and credibility

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Asia Pacific Journal of Marketing and Logistics 2022年第8期35卷 1900-1916页

作者： Wen-Kuo Chen Chia-Ju Ling Chien-Wen Chen Department of Money and Banking National Kaohsiung University of Science and Technology Kaohsiung Taiwan Ph.D. Program in Management Dayeh University Changhua Taiwan Department of Business Administration Feng Chia University College of Business Taichung Taiwan

Purpose This study is based on a web-based advertising value model, adding emotional appeal, advertising credibility, advertising clicks, product evaluation and incentive to examine the impact on consumers' product evaluation and purchase intention. design/methodology/approach In total, 313 valid online questionnaires were collected from those who had browsed the social media advertisement. Findings The results show that advertising credibility and emotional appeal have a higher impact on advertising attitude than informativeness and irritation in the original web-based advertising value model. Research limitations/implications The findings strengthen the literature on the antecedents of advertising attitude in social media context, confirming advertising clicks as a predictor of product evaluation, and determining that advertising attitude, product evaluation and incentive play key roles as predictors of consumer purchase intention. Practical implications The findings are particularly relevant to advertising marketing managers. In the context of social media advertising, the advertising credibility of consumers is the key to increasing consumers' willingness to purchase. Advertisers must establish a good advertising reputation to consumers in the social media context. Originality/value This study combines the web-based advertising value model, advertising clicks, product evaluation and incentive to investigate the factors that influence customers' purchase decisions in the context of social media.

关键词： Advertising value model Ad clicks Product evaluation Incentive Purchase intention

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Analysis on Predictive Water Temperature with PId Controller and SCAdA System

Analysis on Predictive Water Temperature with PID Controller...

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2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021

作者： Tu, Wen-Fu Wang, Yi-Shun Chen, Tu-Sheng National Kaohsiung University of Science and Technology Marit Mechatronics Engineering Lab Department of Marine Engineering Kaohsiung Taiwan National Kaohsiung University of Science and Technology Ph.D. Program In Maritime Science and Technology College of Maritime Kaohsiung Taiwan

ISBN: (纸本)9781665419512

Marine diesel engine start-up goes through the following speed stages: dead slow, slow, half, and full. The jacket water should be heated initially to prevent the cylinder from cracking due to excessive temperature difference. In this paper, PId temperature controller and SCAdA system are used to heat the jacket water. Experimental tests have been evaluated to improve closed loop response with PId temperature controller and SCAdA system. The heating mode calls the appropriate PId controller according to the design temperature to compare the actual temperature at different stages. The experimental results show that the PId controller converges the temperature well between 35and 80, the optimal PId parameter are P=3, I=198, d=495 with the auto tune function and the max. power supply current I=8.8A, voltage V=110V. © 2021 IEEE.

关键词： Three term control systems

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