- 标题
- 摘要
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- 实验方案
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Setting Up Surface-Enhanced Raman Scattering Database for Artificial Intelligence-Based Label-Free Discrimination of Tumor Suppressor Genes
摘要： The quality of input data in deep learning is tightly associated with the ultimate performance of machine learner. Taking advantages of unique merits of surface-enhanced Raman scattering (SERS) methodology in the collection and construction of database (e.g., abundant intrinsic fingerprint information, noninvasive data acquisition process, strong anti-interfering ability, etc.), herein we set up SERS-based database of deoxyribonucleic acid (DNA), suitable for artificial intelligence (AI)-based sensing applications. The database is collected and analyzed by silver nanoparticles (Ag NPs)-decorated silicon wafer (Ag NPs@Si) SERS chip, followed by training with a deep neural network (DNN). As proof-of-concept applications, three kinds of representative tumor suppressor genes, i.e., p16, p21 and p53 fragments, are readily discriminated in label-free manners. Prominent and reproducible SERS spectra of these DNA molecules are collected and employed as input data for DNN learning and training, which enables selective discrimination of DNA target(s). The accuracy rate for the recognition of specific DNA target reaches 90.28%.
关键词： surface-enhanced Raman scattering,label-free discrimination,deep neural network,tumor suppressor genes,artificial intelligence
更新于2025-09-23 15:21:01
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Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System
摘要： Purpose: To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods: An enriched retrospective, fully crossed, multireader, multicase, HIPAA-compliant study was performed. Screening digital mammographic examinations from 240 women (median age, 62 years; range, 39–89 years) performed between 2013 and 2017 were included. The 240 examinations (100 showing cancers, 40 leading to false-positive recalls, 100 normal) were interpreted by 14 Mammography Quality Standards Act–qualified radiologists, once with and once without AI support. The readers provided a Breast Imaging Reporting and Data System score and probability of malignancy. AI support provided radiologists with interactive decision support (clicking on a breast region yields a local cancer likelihood score), traditional lesion markers for computer-detected abnormalities, and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), specificity and sensitivity, and reading time were compared between conditions by using mixed-models analysis of variance and generalized linear models for multiple repeated measurements. Results: On average, the AUC was higher with AI support than with unaided reading (0.89 vs 0.87, respectively; P = .002). Sensitivity increased with AI support (86% [86 of 100] vs 83% [83 of 100]; P = .046), whereas specificity trended toward improvement (79% [111 of 140]) vs 77% [108 of 140]; P = .06). Reading time per case was similar (unaided, 146 seconds; supported by AI, 149 seconds; P = .15). The AUC with the AI system alone was similar to the average AUC of the radiologists (0.89 vs 0.87). Conclusion: Radiologists improved their cancer detection at mammography when using an artificial intelligence system for support, without requiring additional reading time.
关键词： mammography,computer-aided detection,breast cancer,deep learning,artificial intelligence
更新于2025-09-23 15:21:01
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Triboelectric Nanogenerator: A Foundation of the Energy for the New Era
摘要： As the world is marching into the era of the internet of things (IoTs) and artificial intelligence, the most vital development for hardware is a multifunctional array of sensing systems, which forms the foundation of the fourth industrial revolution toward an intelligent world. Given the need for mobility of these multitudes of sensors, the success of the IoTs calls for distributed energy sources, which can be provided by solar, thermal, wind, and mechanical triggering/vibrations. The triboelectric nanogenerator (TENG) for mechanical energy harvesting developed by Z.L. Wang’s group is one of the best choices for this energy for the new era, since triboelectrification is a universal and ubiquitous effect with an abundant choice of materials. The development of self-powered active sensors enabled by TENGs is revolutionary compared to externally powered passive sensors, similar to the advance from wired to wireless communication. In this paper, the fundamental theory, experiments, and applications of TENGs are reviewed as a foundation of the energy for the new era with four major application fields: micro/nano power sources, self-powered sensors, large-scale blue energy, and direct high-voltage power sources. A roadmap is proposed for the research and commercialization of TENG in the next 10 years.
关键词： Internet of Things,Energy Harvesting,Triboelectric Nanogenerators,Artificial Intelligence,Self-Powered
更新于2025-09-23 15:21:01
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Radiative and Nonradiative Tunneling in Nanowire Light-Emitting Diodes
摘要： This paper aims to highlight distinctive features of the SP theory of intelligence, realized in the SP computer model, and its apparent advantages compared with some AI-related alternatives. Perhaps most importantly, the theory simplifies and integrates observations and concepts in AI-related areas, and has potential to simplify and integrate of structures and processes in computing systems. Unlike most other AI-related theories, the SP theory is itself a theory of computing, which can be the basis for new architectures for computers. Fundamental in the theory is information compression via the matching and unification of patterns and, more specifically, via a concept of multiple alignment. The theory promotes transparency in the representation and processing of knowledge, and unsupervised learning of natural structures via information compression. It provides an interpretation of aspects of mathematics and an interpretation of phenomena in human perception and cognition. Abstract concepts in the theory may be realized in terms of neurons and their inter-connections (SP-neural). These features and advantages of the SP system are discussed in relation to AI-related alternatives: the concept of minimum length encoding and related concepts, how computational and energy efficiency in computing may be achieved, deep learning in neural networks, unified theories of cognition and related research, universal search, Bayesian networks and some other models for AI, IBM’s Watson, solving problems associated with big data and in the development of intelligence in autonomous robots, pattern recognition and vision, the learning and processing of natural language, exact and inexact forms of reasoning, representation and processing of diverse forms of knowledge, and software engineering. In conclusion, the SP system can provide a firm foundation for the long-term development of AI and related areas, and at the same time, it may deliver useful results on relatively short timescales.
关键词： information compression,unsupervised learning,perception,reasoning,multiple alignment,cognition,deep learning,mathematics,neural networks,Artificial intelligence
更新于2025-09-23 15:19:57
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Deep learning enabled inverse design in nanophotonics
摘要： Deep learning has become the dominant approach in artificial intelligence to solve complex data-driven problems. Originally applied almost exclusively in computer-science areas such as image analysis and nature language processing, deep learning has rapidly entered a wide variety of scientific fields including physics, chemistry and material science. Very recently, deep neural networks have been introduced in the field of nanophotonics as a powerful way of obtaining the nonlinear mapping between the topology and composition of arbitrary nanophotonic structures and their associated functional properties. In this paper, we have discussed the recent progress in the application of deep learning to the inverse design of nanophotonic devices, mainly focusing on the three existing learning paradigms of supervised-, unsupervised-, and reinforcement learning. Deep learning forward modelling i.e. how artificial intelligence learns how to solve Maxwell’s equations, is also discussed, along with an outlook of this rapidly evolving research area.
关键词： forward modelling,inverse design,nanophotonics,artificial intelligence,metamaterials,machine learning
更新于2025-09-23 15:19:57
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Artificial Intelligence Assisted Mid-Infrared Laser Spectroscopy In Situ Detection of Petroleum in Soils
摘要： A simple, remote-sensed method of detection of traces of petroleum in soil combining artificial intelligence (AI) with mid-infrared (MIR) laser spectroscopy is presented. A portable MIR quantum cascade laser (QCL) was used as an excitation source, making the technique amenable to field applications. The MIR spectral region is more informative and useful than the near IR region for the detection of pollutants in soil. Remote sensing, coupled with a support vector machine (SVM) algorithm, was used to accurately identify the presence/absence of traces of petroleum in soil mixtures. Chemometrics tools such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and SVM demonstrated the effectiveness of rapidly differentiating between different soil types and detecting the presence of petroleum traces in different soil matrices such as sea sand, red soil, and brown soil. Comparisons between results of PLS-DA and SVM were based on sensitivity, selectivity, and areas under receiver-operator curves (ROC). An innovative statistical analysis method of calculating limits of detection (LOD) and limits of decision (LD) from fits of the probability of detection was developed. Results for QCL/PLS-DA models achieved LOD and LD of 0.2% and 0.01% for petroleum/soil, respectively. The superior performance of QCL/SVM models improved these values to 0.04% and 0.003%, respectively, providing better identification probability of soils contaminated with petroleum.
关键词： chemometrics,soil,artificial intelligence (AI),multivariate analysis,mid-infrared (MIR) laser spectroscopy,petroleum,quantum cascade lasers (QCLs)
更新于2025-09-23 15:19:57
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Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra
摘要： Deep learning methods for the prediction of molecular excitation spectra are presented. For the example of the electronic density of states of 132k organic molecules, three different neural network architectures: multilayer perceptron (MLP), convolutional neural network (CNN), and deep tensor neural network (DTNN) are trained and assessed. The inputs for the neural networks are the coordinates and charges of the constituent atoms of each molecule. Already, the MLP is able to learn spectra, but the root mean square error (RMSE) is still as high as 0.3 eV. The learning quality improves significantly for the CNN (RMSE = 0.23 eV) and reaches its best performance for the DTNN (RMSE = 0.19 eV). Both CNN and DTNN capture even small nuances in the spectral shape. In a showcase application of this method, the structures of 10k previously unseen organic molecules are scanned and instant spectra predictions are obtained to identify molecules for potential applications.
关键词： artificial intelligence,excitation spectra,organic molecules,DFT calculations,neural networks
更新于2025-09-19 17:15:36
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New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence
摘要： OBJECTIVE. The purpose of this article is to compare traditional versus machine learning–based computer-aided detection (CAD) platforms in breast imaging with a focus on mammography, to underscore limitations of traditional CAD, and to highlight potential solutions in new CAD systems under development for the future. CONCLUSION. CAD development for breast imaging is undergoing a paradigm shift based on vast improvement of computing power and rapid emergence of advanced deep learning algorithms, heralding new systems that may hold real potential to improve clinical care.
关键词： computer-aided detection,breast,artificial intelligence,mammography,texture analysis,computer-aided diagnosis
更新于2025-09-19 17:15:36
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A Global Maximum Power Point Tracking Algorithm for Photovoltaic Systems Under Partially Shaded Conditions Using Modified Maximum Power Trapezium Method
摘要： This paper is about how the SP theory of intelligence and its realization in the SP machine (both outlined in this paper) may help in the design of the brains of autonomous robots, meaning robots that do not depend on external intelligence or power supplies, are mobile, and have human-like versatility and adaptability in intelligence. This paper addresses three main problems: 1) how to increase the computational and energy efficiency of computers and to reduce their size and weight; 2) how to achieve human-like versatility in intelligence; and 3) likewise for human-like adaptability in intelligence. Regarding the first problem, the SP system has the potential for substantial gains in computational efficiency, with corresponding cuts in energy consumption and the bulkiness of computers: 1) by reducing the size of data to be processed; 2) by exploiting statistical information that the system gathers as an integral part of how it works; and 3) via a new version of Donald Hebb’s concept of a cell assembly. Toward human-like versatility in intelligence, the SP system has strengths in unsupervised learning, natural language processing, pattern recognition, information retrieval, several kinds of reasoning, planning, problem solving, and more, with seamless integration among structures and functions. The SP system’s strengths in unsupervised learning and other aspects of intelligence may help in achieving human-like adaptability in intelligence via: 1) one-trial learning; 2) learning of natural language; 3) learning to see; 4) building 3-D models of objects and of a robot’s surroundings; 5) learning regularities in the workings of a robot and in the robot’s environment; 6) exploration and play; 7) learning major skills; and 8) learning via demonstration. Also discussed are how the SP system may process parallel streams of information, generalization of knowledge, correction of over-generalizations, learning from dirty data, how to cut the cost of learning, and reinforcements and motivations.
关键词： data compression,pattern recognition,robots,unsupervised learning,Artificial intelligence,cognitive science
更新于2025-09-19 17:13:59
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A Gaussian-Gaussian-Restricted-Boltzmann-Machine-based Deep Neural Network Technique for Photovoltaic System Generation Forecasting
摘要： This paper proposes a new Gaussian-Gaussian-Restricted-Boltzmann-Machine-based method for forecasting photovoltaic (PV) system generation forecasting. Although renewable energy such as PV system and wind power generation has been used to suppress greenhouse gases in the world, it has a drawback that weather conditions influence the generation output significantly. Thus, it is not easy to perform Economic Load Dispatch (ELD) and Unit Commitment in power systems smoothly. From a standpoint of power system operation, more accurate predication models are required to deal with predicted values of PV system generation. In this paper, an efficient Deep Neural Network (DNN) model with Gaussian Gaussian Restricted Boltzmann Machine is presented to predict one-step-ahead PV system generation output. The model is based on Restricted Boltzmann Machine as a feature extractor and Multi-Layer Perceptron (MLP) as ANN. The effectiveness of the proposed method is demonstrated for real data of a PV system.
关键词： Solar energy,Forecasting,Time-series analysis,Artificial Intelligence,Power systems
更新于2025-09-19 17:13:59