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- 摘要
- 关键词
- 实验方案
- 产品
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[Advances in Intelligent Systems and Computing] Recent Findings in Intelligent Computing Techniques Volume 709 (Proceedings of the 5th ICACNI 2017, Volume 3) || Detection and Analysis of Oil Spill in Ocean for Reduced Complexity in Extraction Using Image Processing
摘要： Oil spills occurring in oceans are difficult to detect and require sophisticated measures to obtain and analyze the images. In this chapter, both color image using high-resolution cameras and Synthetic Aperture Radar (SAR) images are analyzed and certain useful results are obtained to reduce the complexity in extracting the oil spills. The recognition and examination of the oil spill images are done using image processing technique. Furthermore, if the oil spill is scattered as patches, the algorithm classifies the patches into smaller patches and larger ones by using k-means clustering. Hence, the patches depending on the size or intensity can be extracted on a simpler basis.
关键词： Image processing,Synthetic aperture radar (SAR) images,Machine learning,K-means clustering
更新于2025-09-23 15:21:01
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Detecting task demand via an eye tracking machine learning system
摘要： Computerized systems play a significant role in today’s fast-paced digital economy. Because task demand is a major factor that influences how computerized systems are used to make decisions, identifying task demand automatically provides an opportunity for designing advanced decision support systems that can respond to user needs at a personalized level. A first step for designing such advanced decision tools is to investigate possibilities for developing automatic task load detectors. Grounded in decision making, eye tracking, and machine learning literature, we argue that task demand can be detected automatically, reliably, and unobtrusively using eye movements only. To investigate this possibility, we developed an eye tracking task load detection system and tested its effectiveness. Our results revealed that our task load detection system reliably predicted increased task demand from users’ eye movement data. These results and their implications for research and practice are discussed.
关键词： task demand,human computer interaction,cognitive effort,adaptive decision making,eye tracking,machine learning
更新于2025-09-23 15:21:01
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Machine learning-optimized Tamm emitter for high-performance thermophotovoltaic system with detailed balance analysis
摘要： Light-matter interaction upon nanophotonic structures in the infrared wavelength has drew increasing attentions due to the extensive potential applications. Among them, thermophotovoltaic (TPV) systems can exhibit higher efficiency over the Shockley-Queisser limit due to the nanophotonic structure-enabled tunable narrowband thermal emission rather than the broadband incident spectrum. However, two long-standing issues remain formidable as bottlenecks for achieving better performances of TPV system. One is the competing role of the power density and the system efficiency of TPV system, and the other is the magnanimity possibilities of structures, configurations, dimensions, and materials of thermal emitters that disables the manual optimization of TPV system. Here, we attempt to achieve high-performance TPV system by employing the machine learning algorithm under the framework of material informatics. The power density and system efficiency are well modelled through the detailed balance analysis with full considering the photocurrent generation in the PV cells. Through optimization, the non-trial aperiodic Tamm emitters are obtained and the metal-side one is preferable in terms of the TPV performance. The present work is demonstrated to be feasible and efficient in optimizing the TPV performance, and opens a new door for the optimization problems in other fields.
关键词： Machine learning,Material informatics,Tamm emitter,Optimization,Thermophotovoltaics
更新于2025-09-23 15:21:01
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Decoupling mesoscale functional response in PLZT across the ferroelectric – relaxor phase transition with contact Kelvin probe force microscopy and machine learning
摘要： Relaxor ferroelectrics exhibit a range of interesting material behavior including high electromechanical response, polarization rotations as well as temperature and electric field-driven phase transitions. The origin of this unusual functional behavior remains elusive due to limited knowledge on polarization dynamics at the nanoscale. Piezoresponse force microscopy and associated switching spectroscopy provide access to local electromechanical properties on the micro- and nanoscale, which can help to address some of these gaps in our knowledge. However, these techniques are inherently prone to artefacts caused by signal contributions emanating from electrostatic interactions between tip and sample. Understanding functional behavior of complex, disordered systems like relaxor materials with unknown electromechanical properties therefore requires a technique that allows to distinguish between electromechanical and electrostatic response. Here, contact Kelvin probe force microscopy (cKPFM) is used to gain insight into the evolution of local electromechanical and capacitive properties of a representative relaxor material lead lanthanum zirconate across the phase transition from a ferroelectric to relaxor state. The obtained multidimensional data set was processed using an unsupervised machine learning algorithm to detect variations in functional response across the probed area and temperature range. Further analysis showed formation of two separate cKPFM response bands below 50°C, providing evidence for polarization switching. At higher temperatures only one band is observed, indicating an electrostatic origin of the measured response. In addition, from the cKPFM data qualitatively extracted junction potential difference, becomes independent of the temperature in the relaxor state. The combination of this multidimensional voltage spectroscopy technique and machine learning allows to identify the origin of the measured functional response and to decouple ferroelectric from electrostatic phenomena necessary to understand the functional behavior of complex, disordered systems like relaxor materials.
关键词： phase transition,machine learning,Relaxor ferroelectric,lead lanthanum zirconium titanate,piezoresponse force microscopy,k-means clustering,contact Kelvin probe force microscopy
更新于2025-09-23 15:21:01
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Customizing supercontinuum generation via on-chip adaptive temporal pulse-splitting
摘要： Modern optical systems increasingly rely on complex physical processes that require accessible control to meet target performance characteristics. In particular, advanced light sources, sought for, for example, imaging and metrology, are based on nonlinear optical dynamics whose output properties must often finely match application requirements. However, in these systems, the availability of control parameters (e.g., the optical field shape, as well as propagation medium properties) and the means to adjust them in a versatile manner are usually limited. Moreover, numerically finding the optimal parameter set for such complex dynamics is typically computationally intractable. Here, we use an actively controlled photonic chip to prepare and manipulate patterns of femtosecond optical pulses that give access to an enhanced parameter space in the framework of supercontinuum generation. Taking advantage of machine learning concepts, we exploit this tunable access and experimentally demonstrate the customization of nonlinear interactions for tailoring supercontinuum properties.
关键词： machine learning,optical pulse shaping,supercontinuum generation,photonic chip,nonlinear optics
更新于2025-09-23 15:21:01
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[IEEE SoutheastCon 2018 - St. Petersburg, FL, USA (2018.4.19-2018.4.22)] SoutheastCon 2018 - Designing Light Filters to Detect Skin Using a Low-powered Sensor
摘要： Detection of nudity in photos and videos, especially prior to uploading to the internet, is vital to solving many problems related to adolescent sexting, the distribution of child pornography, and cyberbullying. The problem with using nudity detection algorithms on high fidelity images as a means to combat these problems is that: 1) it implies that a digitized nude photo of a minor already exists (i.e., child pornography), and 2) there are real ethical and legal concerns around the distribution and processing of child pornography. Once a camera captures an image, that image is no longer secure. Therefore, we need to develop new privacy-preserving solutions that prevent the digital capture of nude imagery of minors. Our research takes a first step in trying to accomplish this goal: In this paper, we examine the feasibility of using a low-powered sensor to detect skin dominance (defined as an image comprised of 50% or more of human skin tone) in a visual scene. By designing four custom light filters to enhance the digital information extracted from 300 scenes captured with the sensor (without digitizing high-fidelity visual features), we were able to accurately detect a skin dominant scene with 83.7% accuracy, 83% precision, and 85% recall. Our long-term goal is to design a low-powered vision sensor that can be mounted on a digital camera lens on a teen’s mobile device to detect and/or prevent the capture of nude imagery. Thus, we discuss the limitations of this work toward this larger goal, as well as future research directions.
关键词： filters,skin detection,nudity,low-powered sensor,machine learning
更新于2025-09-23 15:21:01
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Tuning Hydrogenated Si, Ge, and SiGe Nanocluster Properties Using Theoretical Calculations and a Machine Learning Approach
摘要： There are limited studies available that predict the properties of hydrogenated silicon-germanium (SiGe) clusters. For this purpose, we conducted a computational study of 46 hydrogenated SiGe clusters (SixGeyHz, 1<X+Y≤6) to predict the structural, thermochemical, and electronic properties. The optimized geometries of the SixGeyHz clusters were investigated using quantum chemical calculations and statistical thermodynamics. The clusters contained 6 to 9 fused Si-Si, Ge-Ge, or Si-Ge bonds, i.e., bonds participating in more than one 3- to 4-membered rings, and di?erent degrees of hydrogenation, i.e., the ratio of hydrogen to Si/Ge atoms varied depending on cluster size and degree of multifunctionality. Our studies have established trends in standard enthalpy of formation, standard entropy, and constant pressure heat capacity as a function of cluster composition and structure. A novel bond additivity correction model for SiGe chemistry was regressed from experimental data on 7 acyclic Si/Ge/SiGe species to improve the accuracy of the standard enthalpy of formation predictions. Electronic properties were investigated by analysis of the HOMO–LUMO energy gap to study the effect of elemental composition on the electronic stability of SixGeyHz clusters. These properties will be discussed in the context of tailored nanomaterials design and generalized using a machine learning approach.
关键词： Theoretical Calculations,SiGe Nanocluster,Ge,Machine Learning,Hydrogenated Si,Quantum Chemistry,Spectroscopy,Molecular Structure
更新于2025-09-23 15:21:01
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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI, USA (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - InstaBP: Cuff-less Blood Pressure Monitoring on Smartphone using Single PPG Sensor
摘要： Cuff-less Blood Pressure (BP) monitoring has gained interest of the research community in recent years, due to its importance in continuous and non-invasive monitoring of BP for early detection of hypertension, thereby reducing mortality. Several approaches that involve photoplethysmography (PPG) and Pulse Transit Time (PTT) have been explored with promising results; however the requirement of two sensors makes them obtrusive for continuous use. Single PPG sensor approaches using machine learning have also been attempted, but there are certain deficiencies in these methods as they go for a one-size-fits-all approach. In this work, we develop an ensemble of BP prediction models based on demographic and physiological partitioning. Also, we incorporate a set of unique PPG features into our models, which results in test accuracies of 5 mmHg Mean Absolute Error (MAE) for Diastolic BP, and 6.9 mmHg MAE for Systolic BP. Given our marked improvement over ubiquitous models (18% for Diastolic BP and 11.5% for Systolic BP), this approach opens up avenues where single PPG sensor based methods can predict BP with a high degree of accuracy. This is a big step towards developing continuous BP monitoring systems, and can help in better management of cardiac health.
关键词： machine learning,Cuff-less Blood Pressure monitoring,Pulse Transit Time,photoplethysmography,demographic and physiological partitioning
更新于2025-09-23 15:21:01
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Post-Loaded Substrate-Integrated Waveguide Bandpass Filter With Wide Upper Stopband and Reduced Electric Field Intensity
摘要： This paper evaluates the classification of multisample problems, such as electromyographic (EMG) data, by making aggregate features available to a per-sample classifier. It is found that the accuracy of this approach is superior to that of traditional methods such as majority vote for this problem. The classification improvements of this method, in conjunction with a confidence measure expressing the per-sample probability of classification failure (i.e., a hazard function) is described and measured. Results are expected to be of interest in clinical decision support system development.
关键词： Bayes methods,machine learning,statistical learning,pattern analysis,decision support systems,supervised learning
更新于2025-09-23 15:19:57
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[IEEE 2019 International Topical Meeting on Microwave Photonics (MWP) - Ottawa, ON, Canada (2019.10.7-2019.10.10)] 2019 International Topical Meeting on Microwave Photonics (MWP) - On-chip Photonic Method for Doppler Frequency Shift Measurement
摘要： 4-D-computed tomography (4DCT) provides not only a new dimension of patient-specific information for radiation therapy planning and treatment, but also a challenging scale of data volume to process and analyze. Manual analysis using existing 3-D tools is unable to keep up with vastly increased 4-D data volume, automated processing and analysis are thus needed to process 4DCT data effectively and efficiently. In this paper, we applied ideas and algorithms from image/signal processing, computer vision, and machine learning to 4DCT lung data so that lungs can be reliably segmented in a fully automated manner, lung features can be visualized and measured on the fly via user interactions, and data quality classifications can be computed in a robust manner. Comparisons of our results with an established treatment planning system and calculation by experts demonstrated negligible discrepancies (within ±2%) for volume assessment but one to two orders of magnitude performance enhancement. An empirical Fourier-analysis-based quality measure-delivered performances closely emulating human experts. Three machine learners are inspected to justify the viability of machine learning techniques used to robustly identify data quality of 4DCT images in the scalable manner. The resultant system provides a toolkit that speeds up 4-D tasks in the clinic and facilitates clinical research to improve current clinical practice.
关键词： Biomedical image processing,machine learning algorithms,classification algorithms,data visualization,computed tomography,morphological operations,image analysis
更新于2025-09-23 15:19:57