Enclosed cab, 2BBL multi-shank ripper, 2 ripper shanks, s dozer blade with tilts,new engine approx. Good machine. This machine does come with a blade which is not shown. Runs excellent. Call Gary Middlebrooks at Good condition Dresser Td8e Dozers manufactured in Located in USA and other countries.
Click request price for more information. Canada 2. USA Used Dresser dozers - 57 listings. Get email updates for Dresser dozers Email:. Limit this alert to this location:. Contact seller for more information Email. Click to Contact Seller. Please enter your name and company First name. Last name. Next Prev.2005 Dresser TD9H XP dozer with winch C&C Equipment 812-336-2894 wfv.darcarsoutfits.pw
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Submit Prev. Have 5 seconds to help us improve the results? I'll do it.Broken Tractor offers a large selection of new, used, and rebuilt tractor parts online for IH Dozers.
Come browse through all International Dozer Parts via our online catalog. We have a large collection of international harvester parts in our online catalog, but not all are listed on our site. Broken Tractor carries parts for International Dozers and parts for Dresser dozers. We have new non-OEM, rebuilt, and used Dresser crawler parts. We sell engine parts for the International BD diesel engine that was used in the old IH bulldozers. Steering clutch discs, both the fiber and steel discs, are common wear parts.
We also carry used and rebuilt Dresser dozer final drives, engines, and transmission parts. Recently added parts such as engine side shields, water pumps, and alternators can also be found. Track parts for Dresser such as recoil springs, track adjusters, idler guide plates, and roller brackets are built with pride here in the United States and available to ship worldwide. Steering and Brakes. Track Parts. Blade Parts. Final Drive and Transmission.
Hydraulic Parts. Sheet Metal. Root Rake. Dresser Galion Grader. Connect Get the latest updates on new products and upcoming sales Email Address.Dresser Industries was a multinational corporation head quartered in Dallas, Texas, which provided a wide range of technology, products, and services used for developing energy and natural resources. One Division was the Construction machinery division which manufactured loading shovels and Tracked shovels.
It was founded by Solomon Robert Dresser —who manufactured a product that he devised for drillers to keep oil and water separated underground. Dresser created a "packer," using rubber for a tight fit, and after taking out a patent on May 11,he began advertising and selling his product, the Dresser Cap Packerfrom Bradford, Pennsylvania, in the heart of the oilfields.
Dresser's packer was one of many available on the market, and it was another invention that put his company on the map—a coupling that he built in to join pipes together in such a way that they would not leak natural gas.
This coupling also used rubber for a tight fit, and it was so successful that it permitted for the first time the long-range transmission of natural gas from the fields where it naturally occurred to faraway cities.
As the natural gas industry prospered and expanded afterDresser's company grew as pipelines were built over great distances. Following Dresser's death, his descendants decided to sell it, and in the Wall Street investment-banking firm of W.
Harriman and Company, Inc. Neil Mallon was selected as president and chief executive officer; holding that position until his retirement in Under Mallon, a Yale University graduate whose earlier experience had been in the canning industry, Dresser began a program of acquisitions designed to survive a new threat to its coupling business—the introduction of welding for joining pipes together.
Between and the entry of the United States into World War II, Dresser acquired companies that manufactured valves, heaters, pumps, engines and Gas compressor.
After the war, expansion continued, as the company diversified into such products as oil derricks, blowers, drill bits, refractories, and drilling mud. Future United States president George H. Bush worked for the company in several positions after the war, from —, before he founded Zapata Corporation. His father, Prescott Bush, had been a W.
Harriman and Company executive who had been involved in the conversion of Dresser to a public company, and he served on the board of directors for twenty-two years. In the company headquarters moved to Dallas, center of the nation's major oil and gas fields. It continued to purchase well-known companies involved in manufacturing such things as overhead cranes, gasoline-dispensing pumps, and heavy equipment for mining and construction.
During the s, as the oil industry began to decline, Dresser's chairman, John Murphy, began to streamline the organization of the company, deciding to eliminating its insurance, mining and construction -equipment divisions. It struck a joint agreement with Komatsu of Japan in to manufacture construction equipment  such as tractors, loaders, and hydraulic excavators.
The division now belongs to the polish firm HSW fromnow called Dressta. It spun off some of its manufacturing divisions, but crucially agreed to retain asbestos claims filed before the spinoff.
In the company expanded through acquisitions of Wheatley TXT a manufacturer of pumps, valves, and metering equipment and the Baroid Corporation an oil-services firm in Houston that had been a direct competitor. Upon completion of the Baroid merger, Dresser became the third-largest oil-services company in the world. InDresser merged with its main rival Halliburton and is now known as Halliburton Company. InHalliburton was forced to settle the asbestos lawsuits that it acquired as a result of purchasing Dresser, causing the company's stock price to fall by eighty percent in just over a year.
On 10 April the Dresser division excluding the former Kellogg division entered an agreement to separate itself once again from Halliburton by management purchasing its equity, the new company to be called Dresser, Inc. It was planning a new IPO for the summer ofHowever it withdrew its IPO because of accounting problems and an internal investigation of a subsidiaries unauthorized dealings in the Middle East.
Dresser, Inc. The division enter a joint venture with Komatsu of Japan in Sign In Don't have an account? Start a Wiki. Contents [ show ]. List of Construction Plant Manufacturers. Tortone Argentina - Hydromac Argentina. Limited - M. Categories :. Cancel Save. This list is incomplete please add other manufacturers.You may not mix string and numerical values.
If you have binary data in your inputs, you must use base64 encoding to represent it. The following special formatting is required:Your encoded string must be formatted as a JSON object with a single key named b64. You pass input instances for online prediction as the message body for the predict request. For formatting of the request and response body, see the details of the prediction request.
In brief: Make each instance an item in a list, and name the list member instances. You provide input data for batch prediction in one or more text files containing rows of JSON instance data as described above.
An input file contains no column headers or other formatting beyond the simple JSON syntax. This means that your data is distributed among an arbitrary cluster of virtual machines, and is processed in an unpredictable order.
To be able to match the returned predictions with your input instances, you must have instance keys defined. An instance key is a value that every instance has that is unique among the instances in a set of data. The simplest key is an index number. You should pass the keys through your graph unaltered in your training application. If your data doesn't already have instance keys, you can add them as part of your data preprocessing. As new versions of Cloud ML Engine are released, it is possible that models developed against older versions will become obsolete.
This is particularly pertinent if you arrive at an effective model version that remains unchanged for a long period. You should review the Cloud ML Engine versioning policy and make sure that you understand the Cloud ML Engine runtime version that you use to train your model versions.
You can specify a supported Cloud ML Engine runtime version when you create a model version. Doing so establishes the model version's default setting. If you don't specify one explicitly, Cloud ML Engine creates your version using the current default runtime version (typically the most recent stable version).
You can specify a runtime version to use when you start a batch prediction job. This is to accommodate getting predictions using a model that is not deployed on Cloud ML Engine.
You should never use a different runtime version than the default for a deployed model. Doing so is likely to cause unexpected errors. You cannot request online predictions from models outside of Cloud ML Engine, so there is no option to override the default runtime version in your request. The default runtime version set for a model version cannot be changed.
To specify a different runtime version for a model version, deploy a new version using the same training artifacts that you used initially. Google Cloud Platform uses zones and regions to define the geographic locations of physical computing resources. Cloud ML Engine uses regions to designate its processing.
When you deploy a model for prediction, you specify the default region that you want prediction to run in. When you start a batch prediction job, you can specify a region to run the job in, overriding the default region. Online predictions are always served from the region set when the model was created. Batch prediction generates job logs that you can view on Stackdriver Logging.
You can also get logs for online prediction requests if you configure your model to generate them when you create it. You can set online prediction logging for a model by setting onlinePredictionLogging to true (True in Python) in the Model resource you use when creating your model with projects. If you use the gcloud command-line tool to create your model, include the --enable-logging flag when you run gcloud ml-engine models create.
You can request batch prediction using a model that you haven't deployed to the Cloud ML Engine service. Instead of specifying a model or version name, you can use the URI of a Google Cloud Storage location where the model you want to run is stored.
Because an undeployed model doesn't have an established default runtime version, you should explicitly set it in your job request.To update a topic model, you need to PUT an object containing the fields that you want to update to the topic model' s base URL. Once you delete a topic model, it is permanently deleted.
If you try to delete a topic model a second time, or a topic model that does not exist, you will receive a "404 not found" response. However, if you try to delete a topic model that is being used at the moment, then BigML. To list all the topic models, you can use the topicmodel base URL. By default, only the 20 most recent topic models will be returned. You can get your list of topic models directly in your browser using your own username and API key with the following links.
You can also paginate, filter, and order your topic models. Time Series Last Updated: Friday, 2017-10-27 12:23 A time series model is a supervised learning method to forecast the future values of a field based on its previously observed values.
It is used to analyze time based data when historical patterns can explain the future behavior such as stock prices, sales forecasting, website traffic, production and inventory analysis, weather forecasting, etc.
A time series model needs to be trained with time series data, i. BigML implements exponential smoothing to train time series models. Time series data is modeled as a level component and it can optionally include a trend (damped or not damped) and a seasonality components as explained below:Forecast equation Level equation Forecast equation Level equation Trend equation Forecast equation Level equation Damped trend equation Forecast equation Level equation Trend equation Seasonality equation The different components can have variations, e.
As a result of combining the different variations for each component, several models can be trained for a given objective field. Note that BigML excludes certain combinations for numerical stability reasons such as additive errors with multiplicative trends or multiplicative error and trend with additive seasonality. BigML computes four different performance measures to select the best model for a given objective field. You can create a time series model selecting one or several fields from your dataset to use as objective fields to forecast their future values.
You can also list all of your time series. This can be used to change the names of the fields in the time series with respect to the original names in the dataset or to tell BigML that certain fields should be preferred. Example: 100 name optional String,default is dataset's name The name you want to give to the new time series.This trip was a delightful exception. It was very well organized by Nordic Visitor. I felt so much more comfortable with having so much planned by the experts ahead of time.
Dresser Dozer History
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We will be coming back for sure. I thought the hotels which were chosen were excellent. I was also very happy with the planned itinerary. He was very receptive to questions and always got back to me. I can't wait to book with Nordic Visitor NEXT summer. Nordic Visitor was a very efficient way to book our vacation.
The website was easy to navigate and gave all important information needed for us to decide on which trip to take. It was helpful that it included the tours, hotels and travel all in one package. Everything went smoothly and we had no problems at all with our trip.
The tours and places (and order) that were chosen in the package were exceptional. The travel agent we consulted with our questions was helpful, knowledgeable, and quick to respond. I would highly recommend using Nordic Visitor to plan your vacation. My traveling companions and I were very impressed with the ease of our trip due to the wonderfully organized accommodations put together by Nordic Visitor.
We were so happy with every guesthouse we stayed in, as well as the instructions provided for the tours we booked. Booking through Nordic Visitor took all of the stress of planning accommodations out of the equation, leaving us to just enjoy all of the amazing elements of Iceland on our own.
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Unless you're already a well established brand with a must have item, the vast majority of people won't jump at your feet or seek you out to test your latest wares. Likewise if you are a service based industry, you'll find most people will only write about you when they have something bad to say. None of this is good, especially if you want those who are searching for your product online to see a mixture of good reviews.
Having no reviews at all will spark suspicion and negative reviews turn customers away. However, the first step in getting a product or service reviewed is to realise that people don't actually want to review it.
Dresser / International Dozer Parts
When you eat at a restaurant, the waiter will ask you whether you like the food. It's accepted as common practice, but how many times do brands ask us what we think of their products.
It's the same principle, so if I'm using your product, at anytime and anywhere, just ask me what I think. Get customer email addresses at the point of purchase, even if you sell stock offline. Hotel Chocolat give away a small bar of chocolate if you give them your email address in store. Another thing Amazon does well is to use my reviews to recommend other products that I may like in the future. So by reviewing products, I am helping myself to discover other products that I'd like to buy.
Although I'm helping Amazon sell me more stuff by doing so, I still do it. It's a win-win for Amazon, and useful for me as a customer. Before going to market with a product, bringing together a focus group is an essential step for gathering feedback.
Once you've got that data then publish it, write a blog post about it and share your findings. Collect data from customers and turn it into graphs and data sets to show on an Infographic. It's ideal for presenting lots of good customer data in one go. Here's an example of all the aggregated reviews on Yelp that you could do on a smaller scale:Run a poll across your site to get customer feedback and then add this data into a comparison so people can see how you stack up against competitors.
A good example is: Anti-Spyware Reviews An obvious step, but one that is missed a lot. Let people write reviews directly onto your website (it works for any product, not just for e-commerce stores). You'll also find your product pages rank higher by having more unique content.