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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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Implementation of Malaria Detection Using MobileNetV2 Pretrained Model for Improved Deep Learning Based Diagnostics

Implementation of Malaria Detection Using MobileNetV2 Pretra...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Rahmat Hidayat Achmad Solichin Adhitia Erfina Sudin Saepudin Muhamad Muslih Mohd Zainuri Saringat Master of Computer Science Budi Luhur University Jakarta Indonesia Information Systems Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Tun Hussein Onn Malaysia University

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.

关键词： Training Adaptation models Accuracy Costs Malaria Microscopy Data visualization Training data Cost function Data models

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Citrus Limon L. Ripeness Identification Using Feature Extraction of Mean RGB, HSV, LBP and K-Nearest Neighbor

Citrus Limon L. Ripeness Identification Using Feature Extrac...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Ade Cahyo Priyo Utomo Christy Atika Sari Eko Hari Rachmawanto Heru Pramono Hadi Salama A. Mostafa Wisam Subhi Talib Al-Dayyeni Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems University of Dian Nuswantoro Semarang Indonesia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia School of Information Technologies and Engineering ADA University Baku Azerbaijan

Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%.

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Robust H/sub /spl infin// filtering design with pole constraints for discrete-time systems: an LMI approach

Robust H/sub /spl infin// filtering design with pole constra...

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American Control Conference (ACC)

作者： R.M. Palhares P.L.D. Peres Graduate Program in Electrical Engineering Ponificia Universidade Católica de Mines Gerais Belo Horizonte Minas Gerais Brazil DT-School of Electrical and Computer Engineering State University of Campinas-UNICAMP Sao Paulo Brazil

The problem of full order guaranteed cost H/sub /spl infin// filtering design for discrete-time systems with polytope type uncertainty is investigated. The main purpose is to obtain a stable linear filter such that the filtering error system remains quadratically stable within a prespecified H/sub /spl infin// attenuation level. Necessary and sufficient conditions for the existence of such robust filter are provided in terms of linear matrix inequalities (LMIs), which can be efficiently solved by means of high performance convex optimization procedures with global convergence assured. Moreover, as an improvement, the filter dynamics can be constrained to some specific regions inside the unit open disk.

关键词： Robustness Nonlinear filters Riccati equations Attenuation Filtering theory Electronic mail White noise Robust control Uncertainty Output feedback

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Ordinal Classification Method for the Evaluation Of Thai Non-life Insurance Companies

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International Journal of computer Science Issues 2012年第1 1-2期9卷 362-366页

作者： Jhonpita, Phaiboon Sinthupinyo, Sukree Chaiyawat, Thitivadee Technopreneurship and Innovation Management Program Graduate School Chulalongkorn University Bangkok Thailand Department of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand

This paper proposes a use of an ordinal classifier to evaluate the financial solidity of non-life insurance companies as strong, moderate, weak, and insolvency. This study constructed an efficient classification model that can be used by regulators to evaluate the financial solidity and to determine the priority of further examination as an early warning system. The proposed model is beneficial to policy-makers to create guidelines for the solvency regulations and roles of the government in protecting the public against insolvency. © 2012 International Journal of computer Science Issues.

关键词： Insurance

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Surface phase diagram of CsSnI3 from first-principles calculations

arXiv

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arXiv 2024年

作者： Li, Kejia Khamdang, Chadawan Wang, Mengen Department of Electrical and Computer Engineering State University of New York at Binghamton BinghamtonNY13902 United States Materials Science and Engineering Program State University of New York at Binghamton BinghamtonNY13902 United States

CsSnI3 is widely studied as an environmentally friendly Pb-free perovskite material for optoelectronic device applications. To further improve material and device performance, it is important to understand the surface structures of CsSnI3. We generate surface structures with various stoichiometries, perform density functional theory calculations to create phase diagrams of the CsSnI3 (001), (110), and (100) surfaces, and determine the most stable surfaces under a wide range of Cs, Sn, and I chemical potentials. Under I-rich conditions, surfaces with Cs vacancies are stable, which lead to partially occupied surface states above the valence band maximum. Under I-poor conditions, we find the stoichiometric (100) surface to be stable under a wide region of the phase diagram, which does not have any surface states and can contribute to long charge carrier lifetimes. Consequently, the I-poor (Sn-rich) conditions will be more beneficial to improve the device performance. © 2024, CC BY-NC-SA.

关键词： Tin compounds

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NASA's Cost Analysis Steering Group as a Community of Practice

NASA's Cost Analysis Steering Group as a Community of Practi...

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IEEE Conference on Aerospace

作者： Jonathan G. Bryson Steven R. Brill Sheryl McGurk Earth Science Projects Division/420 NASA Goddard Space Flight Center Greenbelt MD USA Engineering and Program Management Division Computer Science Corporation WA USA Cost Analysis Division The Office of Program Analysis and Evaluation WA USA

The National Aeronautics and Space Administration (NASA) and other Federal agencies face a wave of retirements in the near future which could lead to a significant loss of experience and knowledge. As a response, the organizations need to utilize multiple strategies to capture, retain, and transfer knowledge and to maintain skill competency. This paper describes the use of the concept of a "community of practice" (CoP) and the initial steps to develop an Agency-wide discipline group for cost estimation and analysis into a more highly functioning community for the purpose of successful knowledge management. CoP resources from academia and other Federal sources are identified. A new model of a Knowledge Curriculum is also introduced.

关键词： NASA Life estimation Knowledge management Aerospace engineering Knowledge engineering Project management Retirement Cost function Financial management Information analysis

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Wheat Agricultural Residences for Bioenergy Generation in Morocco

Wheat Agricultural Residences for Bioenergy Generation in Mo...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Meisam Mahdavi Francisco Jurado Konrad Schmitt Stephen Bayne Manohar Chamana Department of Electrical Engineering University of Jaén Linares Jaén Spain Electrical & Computer Engineering Department Texas Tech University Lubbock TX USA Renewable Energy Program Texas Tech University Lubbock USA

Morocco is one of the important producers of wheat in Africa, in which this crop is the first agricultural product in the country. In spite of obtaining a huge quantity of wheat residues in the Moroccan agriculture sector, a small portion of this great bioenergy resource is reused for energy generation in the country. This is a big challenge for Morocco because its energy sector highly depends on costly fossil fuel imports. Furthermore, the country has to decrease harmful gases emission based on the Paris and Kyoto agreements. Regarding the importance of rainfall amount and water resources available for this agricultural product that is the main feedstock of Moroccan food, the use of wheat biomass to produce energy will remove high pressure from both the national energy and agriculture sectors. Besides that, the problems related to crop residue removal are reduced by using wheat biomass. Also, the use of wheat residues for energy generation pushes farmers to prevent the open firing of wheat straws and unnecessary air pollutants release. Thus, the present paper, for the first time, calculates possible energy production from wheat straws and root biomasses in Morocco, aiming to provide useful information for Morocco's energy sector.

关键词： Productivity Firing Plants (biology) Hydrogen Crops Air pollution Fossil fuels

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Phortex: Physically-Informed Operational Robotic Trajectories for Scientific Expeditions

IEEE Transactions on Field Robotics

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IEEE Transactions on Field Robotics 2024年 1卷 446-475页

作者： Victoria L. Preston Genevieve E. Flaspohler John W. Fisher Anna P. M. Michel Nicholas Roy Applied Ocean Physics and Engineering Massachusetts Institute of Technology and Woods Hole Oceanographic Institution Joint Program Cambridge MA USA Olin College of Engineering Needham MA USA Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Rhiza Research Oakland CA USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution Woods Hole MA USA

Mobile robots are increasingly used to collect valuable in situ samples during scientific expeditions. However, many phenomena of scientific interest—deep-sea hydrothermal plumes, algal blooms, warm-core eddies, and lava flows—are spatiotemporal distributions that evolve on spatial and temporal scales that complicate sample collection. Here, we consider the problem of charting the space-time dynamics of deep-sea hydrothermal plumes with the state-of-the-art autonomous underwater vehicle (AUV) Sentry. In the hydrothermal plume charting problem, the plume state is driven by complicated and unobserved dynamics in the deep sea. To effectively sample the moving plume, an autonomy system must infer plume dynamics from sparse, point observations, while respecting operational constraints of AUV Sentry that restrict the set of possible trajectories to nonadaptive, uniform-coverage patterns. We frame the plume charting problem as a sequential decision-making problem, and formulate a mission planner Phortex: physically-informed operational robotic trajectories for expeditions that strategically designs full mission trajectories for Sentry, where each mission plan is informed by the observations of the last. Phortex is composed of a trajectory optimizer that maximizes expected samples collected within a moving plume, and Phumes: physically-informed uncertainty models for environment spatiotemporality, a modeling framework that leverages an embedded simulator of idealized plume physics as an inductive bias to enable dynamics learning from extreme partial observations and few Sentry deployments. In both simulation and in field trials at a hydrothermal site in the Gulf of California, we demonstrate that Sentry using Phortex learns to track a moving hydrothermal plume and gather samples that significantly improve upon baseline spatial and temporal diversity for use in downstream science tasks.

关键词： Trajectory Robots Robot sensing systems Vehicle dynamics Spatiotemporal phenomena Predictive models Decision making Uncertainty Data models

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Weakly Supervised Semantic Segmentation using Out-of-Distribution Data

arXiv

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arXiv 2022年

作者： Lee, Jungbeom Oh, Seong Joon Yun, Sangdoo Choe, Junsuk Kim, Eunji Yoon, Sungroh Department Of Electrical And Computer Engineering Seoul National University Korea Republic of NAVER AI Lab University Of Tübingen Germany Department Of Computer Science And Engineering Sogang University Korea Republic of Interdisciplinary Program In AI AIIS ASRI INMC ISRC And NSI Seoul National University Korea Republic of

Weakly supervised semantic segmentation (WSSS) methods are often built on pixel-level localization maps obtained from a classifier. However, training on class labels only, classifiers suffer from the spurious correlation between foreground and background cues (e.g. train and rail), fundamentally bounding the performance of WSSS. There have been previous endeavors to address this issue with additional supervision. We propose a novel source of information to distinguish foreground from the background: Out-of-Distribution (OoD) data, or images devoid of foreground object classes. In particular, we utilize the hard OoDs that the classifier is likely to make false-positive predictions. These samples typically carry key visual features on the background (e.g. rail) that the classifiers often confuse as foreground (e.g. train), so these cues let classifiers correctly suppress spurious background cues. Acquiring such hard OoDs does not require an extensive amount of annotation efforts;it only incurs a few additional image-level labeling costs on top of the original efforts to collect class labels. We propose a method, W-OoD, for utilizing the hard OoDs. W-OoD achieves state-of-the-art performance on Pascal VOC 2012. The code is available at: https://***/naver-ai/w-ood. © 2022, CC BY-NC-SA.

关键词： Semantic Segmentation

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