123 skills found · Page 4 of 5
Pau0031 / DMTO Catalyst Carbon Deposition EDBR JITLDimethyl ether/Methanol to Olefins (DMTO) is one of the important unit in coal chemical industry, and the distribution of its reaction products can be regulated and optimized by catalyst carbon deposition. Aiming at the disadvantages of time-consuming and high-cost analysis of traditional catalyst carbon deposition measurement methods, a Just-in-Time Learning (JITL) soft sensor model based on Euclidean Distance similarity and Bayesian Regression local model (EDBR-JITL) is proposed for DMTO catalyst carbon deposition in this paper. In the proposed model, the mapping relationship between process state, operating parameters, and catalyst carbon deposition is established, and catalyst carbon deposition is predicted at sampling time by a linear local model (BR) which is obtained by the ED similarity. The results of an industrial DMTO example show that the mean absolute error (MAE) between the predicted value of EDBR-JITL online and the real value of measurement offline for catalyst carbon deposition is 0.10, the mean absolute percentage error (MAPE) is 1.46%, the coefficient of determination (R 2 ) is 0.92, and the prediction time is only in milliseconds. In summary, catalyst carbon deposition in DMTO process is not only predicted and monitored online accurately and conveniently by the proposed EDBR-JITL method, but also is regulated and optimized as the linear BR local model can return the weight of each input parameter at each sampling time.
glynnbird / MdOffline-first, PouchDB-powered, Markdown word processor app.
yarosl4v / FilterAIThe app lets users to process Neural Style Transfer offline!
cevin / BtcdOffline Bitcoin transaction processor , simple self-hosted Bitcoin payment support (create and parse address, create and sign offline transaction)
carlos-vf / Fish Detection Tracking And ClassificationA robust computer vision pipeline for underwater fish analysis. It uses a YOLOv8 model for stable, real-time tracking and classification. Features multiple processing modes, including buffered real-time analysis and high-accuracy offline filtering, making it a flexible tool for marine biologists and researchers.
Gtwizzy / ESP32 S3 Co2 Monitor For ESPHome And Home AssistantA DIY Home Assistant–compatible CO₂ sensor and display powered by ESPHome, featuring a Sensirion SCD41 sensor and a 1.28" round LCD. Designed for accurate indoor air-quality monitoring with local processing, stylish visuals, and fully offline operation.
bharathbhimshetty / Denoising Dirty Documents# Denoising Dirty Documents Optical Character Recognition (OCR) is the process of getting type or handwritten documents into a digitized format. If you've read a classic novel on a digital reading device or had your doctor pull up old healthcare records via the hospital computer system, you've probably benefited from OCR. OCR makes previously static content editable, searchable, and much easier to share. But, a lot of documents eager for digitization are being held back. Coffee stains, faded sun spots, dog-eared pages, and lot of wrinkles are keeping some printed documents offline and in the past. This competition challenges you to give these documents a machine learning makeover. Given a dataset of images of scanned text that has seen better days, you're challenged to remove the noise. Improving the ease of document enhancement will help us get that rare mathematics book on our e-reader before the next beach vacation. We've kicked off the fun with a few handy scripts to get you started on the dataset. Acknowledgements Kaggle is hosting this competition for the machine learning community to use for fun and practice. This dataset was created by RM.J. Castro-Bleda, S. España-Boquera, J. Pastor-Pellicer, F. Zamora-Martinez. We also thank the UCI machine learning repository for hosting the dataset. If you use the problem in publication, please cite: Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science ## AIM: * To Denoise the images using Encoder-Decoder Model ## Dataset: * https://www.kaggle.com/c/denoising-dirty-documents/data * We are provided two sets of images, train and test. These images contain various styles of text, to which synthetic noise has been added to simulate real-world, messy artifacts. The training set includes the test without the noise (train_cleaned). You must create an algorithm to clean the images in the test set.
Saiteja-Pagadala / Clustering Data Streams Based On Shared Density Between Micro ClustersAs more and more applications produce streaming data, clustering data streams has become an important technique for data and knowledge engineering. A typical approach is to summarize the data stream in real-time with an online process into a large number of so called micro-clusters. Micro-clusters represent local density estimates by aggregating the information of many data points in a defined area. On demand, a (modified) conventional clustering algorithm is used in a second offline step to re-cluster the micro clusters into larger final clusters. For re-clustering, the centers of the micro-clusters are used as pseudo points with the density estimates used as their weights. However, information about density in the area between micro-clusters is not preserved in the online process and re-clustering is based on possibly inaccurate assumptions about the distribution of data within and between micro-clusters (e.g., uniform or Gaussian). This paper describes DBSTREAM, the first micro-cluster-based online clustering component that explicitly captures the density between micro-clusters via a shared density graph. The density information in this graph is then exploited for re-clustering based on actual density between adjacent micro-clusters. We discuss the space and time complexity of maintaining the shared density graph. Experiments on a wide range of synthetic and real data sets highlight that using shared density improves clustering quality over other popular data stream clustering methods which require the creation of a larger number of smaller micro clusters to achieve comparable results.
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johnny04thomas / Rgbd Stereo 360cameraThis repository contains scripts for offline processing of raw data acquired from stereo spherical cameras (ex. Ricoh THETA) to obtain rgbd image sequences which can later be used for various SLAM functionalities.
VatanaChhorn / KhmerCoreMLSMSClassifierA Swift package for on-device classification of Khmer SMS messages using CoreML. Features lightweight, offline processing with specialized Khmer language text preprocessing.
ZohaibKhanDev / Instagram Video DownloaderInstagram Video Downloader is a versatile app that simplifies the process of downloading Instagram videos and converting them into various formats. Whether you want to save a video for offline viewing or extract audio as an MP3, this app has you covered. With a user-friendly interface, you can easily input video URLs, view thumbnails, and choose yo
sipeter / Zian PublicA fast, highly accurate, and 100% offline voice productivity tool. Keep your data private with local Speech-to-Text processing and use your phone as a wireless mic via the Android companion app.
OCEANOFANYTHINGOFFICIAL / Writewrl## Writeurl - an online collaborative editor ### Writeurl is a collaborative editor Writeurl is a client server system. The frontend code is written in pure javascript using no frameworks. The backend is a node.js application. The client and server communicate through a WebSocket connection. The client stores local changes in the browser's local storage. The editor can be used in offline mode. Changes are always uploaded to the server when a connection is available. Writeurl documents are identified by their (write)url: www.writeurl.com/text/id/read-password/write-password There is a read only version with a (read)url of the form www.writeurl.com/text/id/read-password This url structure makes it easy to share documents. No user registration is needed. #### Writeurl as an online service Writeurl is available as an online service at www.writeurl.com The code running the online service is the same as in this git repo. ###Local installation Writeurl can be installed and run locally as an alternative to using the online service. #### Installation instructions ##### Dependencies The only required dependeny is node.js and the modules in package.json. It is recommended to use node.js version 8. ##### Clone the repo ``` git clone https://github.com/morten-krogh/writeurl.git ``` ##### Install the node js modules ``` npm install ``` ##### Build the browser code Go to the writeurl directory. Use the build script. ``` bash build.sh browser ``` Now the browser code is available in the directory `build/release/browser/` ##### Configuring the server The node.js server code is located in the directory `server-nodejs-express` The server needs a configuration file in the YAML format. An example file is ```server-nodejs-express/config.yaml``` ``` port: 9000 release: public: /Users/mkrogh/writeurl/build/release/browser debug: host: debug.writeurl.localhost public: /Users/mkrogh/writeurl/build/debug/browser documents: path: /Users/mkrogh/writeurl-test-dir/doc_dir publish: public: /Users/mkrogh/writeurl-test-dir/publish_dir # Pino logger # Output goes to stdout. logger: # levels: silent, fatal, error, warn, info, debug, trace level: trace ``` `port` is the port at which the server listens. `release` and `debug` are the directories built above containing the browser code. `documents` is the directory where all the writeurl documents will be stored. The server uses files to store the documents (one subdirectory per document). `publish` is the directory where the published (html) versions of the documents will be stored. `logger.level` is the logging level. Logging goes to standard output. ##### Starting the server In the directory `server-nodejs-express` type ```node writeurl-server.js config.yaml ``` ##### Start typing Go to localhost:9000 in the browser and start typing. ### Example production installation For production, it is recommended to use a reverse proxy with TLS, and to daemonize the Writeurl server. For daemonization, one can use a node.js process manager or a system daemon such as systemd on Linux. The online Writeurl service uses nginx as a reverse proxy and systemd under Linux for daemonization. ##### Example nginx configuration An example nginx.conf file is located at https://github.com/morten-krogh/writeurl/blob/master/documentation/nginx.conf ##### Example systemd unit file ``` [Unit] Description = writeurl server After = network.target [Service] Type = simple User = www ExecStart = /home/www/.nvm/versions/node/v8.9.4/bin/node /home/www/writeurl/server-nodejs-express/writeurl-server.js /home/www/writeurl/server-nodejs-express/config-debian.yaml Restart = on-failure [Install] WantedBy = multi-user.target ``` ### Backup The server can be backed up by just backing up the files in the `documents` and `publish` directories specified in the config.yaml file. Any type of file backup can be used, e.g. periodic rsync. The backup script can be used while the server is running as long as the backup script does not change any files. The server can be restarted from a backup by just placing the backup directories in the place pointed to by `documents` and `publish` in the config file. ### Embedding Writeurl can be embedded as described in https://github.com/morten-krogh/writeurl/blob/master/html/embed/index.html This page is available on the online service as well https://www.writeurl.com/embed ### Contributions and issues Bugs and feature requests are appreciated and can be opened as Github issues. We also welcome code contributions as pull requests.
JorritMontijn / EyeTrackerOnline pupil detection, video acquisition, and offline processor with SpikeGLX synchronization
balena-io-experimental / Balena Pm2 MonitorUse pm2 (process manager) to run your scripts forever and notify you if your device goes offline.
jatinnx777 / EMERGENCY DETECTORAI Voice Keyword Detector is an offline Python-based tool that listens for predefined voice commands like "help" or "alert". It captures audio in real-time, detects keywords using efficient signal processing, and triggers custom actions instantly designed for privacy, fast response, and offline use.
geniuszly / GenWebDownloaderis a Node.js application designed to download entire websites and their resources for offline viewing. It utilizes Puppeteer for browser automation and Axios for resource fetching, ensuring a complete and efficient download process.
liennh / ViPass Password KeeperThe best password manager 2018. Trusted by hundred of thousands people. ViPass is designed by 1Pass Production team. We made it for iPhone, iPad, iPod, iMac and Macbook Pro. Sync data across devices. ViPass keep safe your sensitive information in one vault. You only need to remember 1 master password to unlock ViPass. All other sensitive micro-data is in your hand anytime - anywhere. What is ViPass app? - The Best Password Generator - The Best Password Manager - The Best Password Keeper - A Secure Password Vault - A Secure Digital Wallet - #1 FREE Password App Why should I use a password manager? Passwords are everywhere. We need them to access our computers, our email accounts and many other services that we rely on. Unfortunately, good passwords are hard to remember and in the digital age we live in it has become practically impossible to keep track of all the user names and passwords we use each day. Most people try to mitigate this by composing their passwords out of familiar things like names or birthdays, but these can be guessed easily and do not provide sufficient protection. Password reuse, which is also wide-spread, is even more dangerous and makes a lot of people easy targets for hackers. Padlock helps you keep track of all your sensitive information by storing it in a secure, encrypted vault that is protected by a single master password. Do we know your passwords? NO. We CANNOT. Why? Because all your data is encrypted AES 256-bit using the key derived from your MASTER PASSWORD and PRIVATE KEY. We utilize PBKDF2 and HMAC-SHA256 to generate your key. So it's very secure. Even our server is hacked. Hackers CANNOT decrypt it. The encryption is End-to-End. It means happening at Client app before sending data over network. Our cloud contains only random data. You also can choose to use our app offline. Then backup and sync later if needed. Why is ViPass the best password manager? We make ViPass with the whole our love and energy. ViPass has been made by an A-Class Team. We focus on 3 things: ***** Simple and Usability ***** Unlike other password manager apps with rich of many features and complex GUI, ViPass is so easy to use. We know what you want to do and we focus on you for designing, testing, running. ***** Security ***** Our data center is located in US, San Francisco. ViPass keep safe your micro-data by encrypting it with AES 256-bit, using HMAC-SHA256 and PBKDF2 for generating keys. Only you with master password and private key can open it. All encryption processing happens in client side. We never send your master password to server or store else where. Everything on cloud is only random data. Just look at ViPass White Paper for more details. ***** Performance ***** Unlike other apps built by Javascript for saving cost, ViPass app and its cloud is written in Swift - A programming language designed by Apple and open source. So it is a NATIVE app, running FAST and smoothly. In reality, it only takes some seconds to backup and sync all micro-data on a new device.
samiurprapon / BraSizePredictionThis is such a interesting project based on Neural net and image processing. Later on, it will be ported on android devices to predict real time Bra size. App will work offline. Respecting user privacy, it won't collect any sensitive informations.
