Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health of a system or component is determined based on the observations; and a prediction problem, in which, using the model, the state-parameter distribution is simulated forward in time to compute end of life and remaining useful life. The first problem is typically solved through the use of a state observer, or filter. The choice of filter depends on the assumptions that may be made about the system, and on the desired algorithm performance. In this paper, we review three separate filters for the solution to the first problem: the Daum filter, an exact nonlinear filter; the unscented Kalman filter, which approximates nonlinearities through the use of a deterministic sampling method known as the unscented transform; and the particle filter, which approximates the state distribution using a finite set of discrete, weighted samples, called particles. Using a centrifugal pump as a case study, we conduct a number of simulation-based experiments investigating the performance of the different algorithms as applied to prognostics.

Report Filter Definitions and Guidance Please note that all filter options are present in the dataset. For example, if you are looking at a dataset and a state is missing, it means there is no data for the year selected in that state - it does not use a list of all US states. Also note that if the data table disappears, there is no data available for the filter selections made.

This document contains data on the gyroscope, accelerometer, and complementary filters.

Russia Avg Consumer Price: Tobacco: Cigarettes: Foreign Brands: with Filter data was reported at 131.420 RUB/Pack in Jan 2019. This records an increase from the previous number of 130.140 RUB/Pack for Dec 2018. Russia Avg Consumer Price: Tobacco: Cigarettes: Foreign Brands: with Filter data is updated monthly, averaging 27.510 RUB/Pack from Jan 1995 (Median) to Jan 2019, with 289 observations. The data reached an all-time high of 131.420 RUB/Pack in Jan 2019 and a record low of 1.390 RUB/Pack in Jan 1995. Russia Avg Consumer Price: Tobacco: Cigarettes: Foreign Brands: with Filter data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PA007: Average Consumer Price: Tobacco.

Data Requirement Date Filter describes a required data item for evaluation in terms of the type of data, and date-based filters for that data item. It refers to a constraint of the Data Requirement Structure.

This contains two datasets used for demonstrate the dangers and solutions for post search filter in crosslinking mass-spectrometry. A dataset of search results for mycoplasma pneumonia acquisitions and escherichia coli. Both where searched against a combined database of all e.coli and mycoplasma pneumonia proteins.

These are results without any cut-off and are the base of testing if a filter or processing step is actually affecting decoys differently then target false positives. The idea being that these search results provide an decoy independent set of known false positive matches; all matches involving e.coli peptides to mycoplasma spectra and all matches involving mycoplasma pneumonia peptides to e.coli spectra. In the original use case the data where used to detect if a filter, that uses match external information to filter individual matches, results in an underrepresentation of decoys when compared to these secondary known false positives and how at least no contradiction was found when applying teh ec-filter style of applying the information.

The second dataset is a set of FDR results for 2% unique residue pair FDR of an yeast 26S Proteasome acquisition run with and without using the ec-filter and each of tzhese with and without xiFDR in built boosting. The spectra where searched against increasingly larger databases to show the effect of filtering the results depending on the database size – both in terms of present and assumed non-present proteins.

This page provides instructions on using the platform's Global Filter feature.

Check out our data lens page for additional data filtering and sorting options: https://data.cityofnewyork.us/view/i4p3-pe6a

This dataset contains Open Parking and Camera Violations issued by the City of New York. Updates will be applied to this data set on the following schedule:

New or open tickets will be updated weekly (Sunday). Tickets satisfied will be updated daily (Tuesday through Sunday). NOTE: Summonses that have been written-off are indicated by blank financials.

Summons images will not be available during scheduled downtime on Sunday - Monday from 1:00 am to 2:30 am and on Sundays from 5:00 am to 10:00 am.

• Initial dataset loaded 05/14/2016.

361208 Global import shipment records of Filter Elements with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

A complete list of live websites using the Wp Meta Data Filter And Taxonomy Filter technology, compiled through global website indexing conducted by WebTechSurvey.

anthracite-org/Stheno-Data-Filtered dataset hosted on Hugging Face and contributed by the HF Datasets community

Content

The two CSV files here are the train and test data in Kaggle's Ion Switching Competition with drift removed and filter with Kalman filter to reduce noise.

Acknowledgements

This ideas where posted by @cdeotte and @teejmahal20, I just run the filter and the FE and save the data.

Many diagnostic datasets suffer from the adverse effects of spikes that are embedded in data and noise. For example, this is true for electrical power system data where the switches, relays, and inverters are major contributors to these effects. Spikes are mostly harmful to the analysis of data in that they throw off real-time detection of abnormal conditions, and classification of faults. Since noise and spikes are mixed together and embedded within the data, removal of the unwanted signals from the data is not always easy and may result in losing the integrity of the information carried by the data. Additionally, in some applications noise and spikes need to be filtered independently. The proposed algorithm is a multi-resolution filtering approach based on Haar wavelets that is capable of removing spikes while incurring insignificant damage to other data. In particular, noise in the data, which is a useful indicator that a sensor is healthy and not stuck, can be preserved using our approach. Presented here is the theoretical background with some examples from a realistic testbed.

RIP is a method for perference data filtering. The core idea is that low-quality input prompts lead to high variance and low-quality responses. By measuring the quality of rejected responses and the reward gap between chosen and rejected preference pairs, RIP effectively filters prompts to enhance dataset quality. We release 4k data that filtered from 20k Wildchat prompts. For each prompt, we provide 64 responses from Llama-3.1-8B-Instruct and their corresponding rewards obtained from ArmoRM.… See the full description on the dataset page: https://huggingface.co/datasets/facebook/Wildchat-RIP-Filtered-by-8b-Llama.

Each unique combination of a lobbyist, his/her employer, and a client of that employer. This dataset can be used to see relationships between these three entities. Each has a separate dataset with more detailed information about each lobbyist, employer, or client. See http://www.cityofchicago.org/city/en/depts/ethics/provdrs/lobby.html for more information on the Board of Ethics' role in regulating and reporting on lobbying in Chicago.

Bagging (i.e., bootstrap aggregating) involves combining an ensemble of bootstrap estimators. We consider bagging for inference from noisy or incomplete measurements on a collection of interacting stochastic dynamic systems. Each system is called a unit, and each unit is associated with a spatial location. A motivating example arises in epidemiology, where each unit is a city: the majority of transmission occurs within a city, with smaller yet epidemiologically important interactions arising from disease transmission between cities. Monte Carlo filtering methods used for inference on nonlinear non-Gaussian systems can suffer from a curse of dimensionality as the number of units increases. We introduce bagged filter (BF) methodology which combines an ensemble of Monte Carlo filters, using spatiotemporally localized weights to select successful filters at each unit and time. We obtain conditions under which likelihood evaluation using a BF algorithm can beat a curse of dimensionality, and we demonstrate applicability even when these conditions do not hold. BF can out-perform an ensemble Kalman filter on a coupled population dynamics model describing infectious disease transmission. A block particle filter also performs well on this task, though the bagged filter respects smoothness and conservation laws that a block particle filter can violate.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

all implementation measurements given here were carried out.

The water bottle detection dataset and measurement model dataset for the paper titled "The Semantic PHD Filter for Multi-class Target Tracking: From Theory to Practice" by Jun Chen, Zhanteng Xie and Philip Dames, and the paper titled "Experimental Datasets and Processing Codes for the Semantic PHD Filter" by Zhanteng Xie, Jun Chen and Philip Dames

1. Detection dataset:

Size: Total: 4870 images Training: 4000 images Validation: 870 images

Bottle Classes: Aquafina, Deer, Kirkland, Nestle

Format: PASCAL VOC, Darknet

Folder Structure: - Annotations: containing the xml label files in PASCAL VOC format - ImageSets: containing the training index files - JPEGImages: containing the image data in jpg format - Labels: containing the txt label files in Darknet format

1. Measurement model dataset:

Format: ROSBAG

Duration: 19:59s (1199s)

Topics: /darknet_ros/detection_image 3543 msgs : sensor_msgs/Image /map 1 msg : nav_msgs/OccupancyGrid /sphd_measurements 3585 msgs : sphd_msgs/SPHDMeasurements /tf 142727 msgs : tf2_msgs/TFMessage /tf_static 1 msg : tf2_msgs/TFMessage

Message Types: nav_msgs/OccupancyGrid sensor_msgs/Image sphd_msgs/SPHDMeasurements tf2_msgs/TFMessage

1. Processing codes:

Detection processing: Zenodo: https://doi.org/10.5281/zenodo.7066045 GitHub: https://github.com/TempleRAIL/yolov3_bottle_detector

Measurement model processing: Zenodo: https://doi.org/10.5281/zenodo.7066050 GitHub: https://github.com/TempleRAIL/sphd_sensor_models

The Hard Disk Drive (HDD) filter market is experiencing steady growth, driven by the increasing demand for data storage and the rising adoption of HDDs across various applications. The market, estimated at $500 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $850 million by 2033. This growth is fueled by several key factors. The expanding data center infrastructure globally necessitates higher storage capacity, directly impacting HDD filter demand. Furthermore, the increasing focus on data security and reliability within these data centers necessitates high-quality filtration systems to protect sensitive hardware from environmental contaminants. Technological advancements in filter designs, such as the development of more efficient recirculating filters and improved breather filter materials, also contribute to market expansion. Segment-wise, the recirculating filters segment currently holds a larger market share due to their superior filtering capabilities and longer lifespan, compared to breather filters. Geographically, North America and Asia Pacific are the leading regions, driven by robust technological advancements and significant investments in data storage infrastructure. However, emerging economies in regions like the Middle East and Africa are expected to show significant growth in the coming years, owing to increasing digitalization and infrastructure development. Market restraints include the increasing adoption of Solid State Drives (SSDs) and the potential for price fluctuations in raw materials used in filter manufacturing. The competitive landscape is characterized by a mix of established players such as Donaldson, 3M, and Pall Corporation, and specialized regional manufacturers. These companies are investing heavily in research and development to enhance their product offerings, focusing on miniaturization, improved efficiency, and longer filter lifespans. This innovation is crucial for staying competitive and meeting the ever-evolving demands of the data storage industry. Strategic partnerships and acquisitions are also prevalent strategies among key players seeking to expand their market reach and product portfolio. Overall, the HDD filter market presents significant opportunities for growth and innovation, driven by a growing demand for reliable and efficient data storage solutions. However, manufacturers need to adapt to the changing market dynamics by focusing on innovation and cost-optimization to maintain profitability and market share.