Introduction
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java学习记录
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Some study notes on java and python. Java learning comparison includes java basic syntax, java multi-threading, design patterns and Spring annotations. Python study notes include Python basic syntax, machine learning related libraries, and Anaconda tools. The basic Python syntax includes multi-process, multi-corout, decorator, Pythonic methods, generators and iterators, as well as some advanced tips. The machine learning library mainly includes matplotlib, numpy, pandas, scikit, skimage and other materials.
javaとpythonに関するいくつかの研究ノート。 Javaの学習比較には、Javaの基本構文、Javaのマルチスレッド、デザインパターン、およびSpringの注釈が含まれます。 Pythonスタディノートには、Pythonの基本的な構文、機械学習関連のライブラリ、およびAnacondaツールが含まれています。 Pythonの基本的な構文には、マルチプロセス、マルチコルート、デコレータ、Pythonicメソッド、ジェネレータ、イテレータ、およびいくつかの高度なヒントがあります。 機械学習ライブラリには、主にmatplotlib、numpy、pandas、scikit、skimage、その他の資料が含まれています。
Деякі нотатки про дослідження на Java і Python. Порівняння Java навчання включає базовий синтаксис Java, багатопоточність Java, шаблони дизайну та анотації Spring. Зауваження Python включають базовий синтаксис Python, бібліотеки, пов'язані з машинним навчанням, і інструменти Anaconda. Основний синтаксис Python включає в себе багатопроцесорний, багатокорабельний, декоратор, Pythonic методи, генератори і ітератори, а також деякі вдосконалені поради. Бібліотека машинного навчання в основному включає в себе matplotlib, numpy, pandas, scikit, skimage та інші матеріали.
જાવા અને પાયથોન પર કેટલાક અભ્યાસો નોંધે છે. જાવા લર્નિંગ સરખામણીમાં જાવા બેઝિક સિન્ટેક્સ, જાવા મલ્ટિ-થ્રેડિંગ, ડિઝાઇન પેટર્ન અને સ્પ્રિંગ એન્ટેશન્સ શામેલ છે. પાયથોન અભ્યાસ નોંધો પાયથોન મૂળભૂત વાક્યરચના, મશીન લર્નિંગ સંબંધિત પુસ્તકાલયો, અને એનાકોન્ડા સાધનોનો સમાવેશ કરે છે. મૂળભૂત પાયથોન સિન્ટેક્સમાં મલ્ટિ-પ્રોસેસ, મલ્ટિ-કોરઆઉટ, ડેકોનર, પાયથોનિક પદ્ધતિઓ, જનરેટર અને ઇટરેટર્સ, તેમજ કેટલીક વિગતવાર ટીપ્સ શામેલ છે. મશીન લર્નિંગ લાઇબ્રેરીમાં મુખ્યત્વે મેટપ્લોલિબ, numpy, pandas, scikit, skimage અને અન્ય સામગ્રી શામેલ છે.
[1] J. Yin, Y. Huang, B. Chen and S. Ye, "Color Transferred Convolutional Neural Networks for Image Dehazing," in IEEE Transactions on Circuits and Systems for Video Technology. doi: 10.1109/TCSVT.2019.2917315 keywords: {Atmospheric modeling;Image color analysis;Integrated circuit modeling;Estimation;Standards;Computational modeling;Image dehazing;color transfer;deep learning}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8716692&isnumber=4358651
[2] Y. Wu, H. Tsai, Y. Huang and B. Chen, "Accurate Emotion Recognition for Driving Risk Prevention in Driver Monitoring System," 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE), Nara, 2018, pp. 796-797. doi: 10.1109/GCCE.2018.8574610 keywords: {convolution;driver information systems;emotion recognition;face recognition;feature extraction;feedforward neural nets;driving risk prevention;driver monitoring system;DeriskNet;deep convolutional neural network;audio resources;driving risks;facial emotion recognition;convolutional neural network designs;Vehicles;Emotion recognition;Monitoring;Convolution;Convolutional neural networks;Computer science;Sensors;driver monitoring;driving risks;negative emotions}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8574610&isnumber=8574475
[3] Y. Pang, S. Syu, Y. Huang and B. Chen, "An Advanced Deep Framework for Recognition of Distracted Driving Behaviors," 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE), Nara, 2018, pp. 802-803. doi: 10.1109/GCCE.2018.8574512 keywords: {behavioural sciences computing;convolution;driver information systems;feedforward neural nets;image recognition;road safety;deep convolutional neural networks;DedistractedNet;advanced deep framework;trainable deep framework;distracted driving behavior recognition;physiological sensors;on-board diagnostics;baseline CNN methods;Convolution;Image recognition;Measurement;Physiology;Sensors;Convolutional neural networks;Vehicles;driving behaviors;deep learning;image recognition}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8574512&isnumber=8574475
[4] J. Su, Y. Huang, J. Yin, B. Chen and S. Qu, "Saliency-Guided Deep Framework for Power Consumption Suppressing on Mobile Devices," 2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII), Jeju, 2018, pp. 191-194. doi: 10.1109/ICKII.2018.8569207 keywords: {image enhancement;image texture;mobile handsets;neural nets;telecommunication power management;visual perception;textual features;saliency-guided deep framework;power consumption suppressing;mobile devices;trainable power-constrained contrast enhancement algorithm;imaging features;displayed image;human visual perception;deep convolutional neural networks;perceptual quality;Power demand;Imaging;Convolution;Organic light emitting diodes;Energy efficiency;Technological innovation;Visualization;mobile devices;power consumption;deep framework}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8569207&isnumber=8569041
[5] Y. Huang, J. Yin, B. Chen and S. Ye, "Impaired driving detection based on deep convolutional neural network using multimodal sensor data," 2018 IEEE International Conference on Applied System Invention (ICASI), Chiba, 2018, pp. 957-960. doi: 10.1109/ICASI.2018.8394429 keywords: {driver information systems;feature extraction;feedforward neural nets;road accidents;road safety;sensor fusion;deep convolutional neural network;multimodal sensor data;intelligent vehicle systems;advanced driving assistance systems;driving-related accidents;human injuries;impaired driving detection;collaborative information extraction;classifier;feature fusion;Feature extraction;Vehicles;Convolution;Sensors;Conferences;Accidents;Support vector machines;intelligent vehicle systems;multimodal sensor data;deep neural network}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8394429&isnumber=8394245