Air National Guard gets service’s first combat-ready F-15EX

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  6 mars 2024 | International, Sécurité

  China unveils new defense budget, with a 7.2% increase

  China’s military expenditure is about four times that of Japan and around 12 times larger than that of Taiwan.

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  17 janvier 2019 | International, Aérospatial, Naval, Terrestre, C4ISR, Sécurité

  Contract Awards by US Department of Defense - January 16, 2019

  ARMY Caddell Construction Co. (DE) LLC., Montgomery, Alabama, was awarded a $143,514,000 firm-fixed-price contract for the construction of an airmen training complex dormitory, a dining and classroom facility, supporting facilities, a free standing equipment building, a weapons cleaning pavilion, running track, exercise pads and parking lots. Bids were solicited via the internet with two received. Work will be performed in San Antonio, Texas, with an estimated completion date of June 8, 2021. Fiscal 2019 military construction funds in the amount of $143,514,000 were obligated at the time of the award. U.S. Army Corps of Engineers, Fort Worth, Texas, is the contracting activity (W9126G-19-C-0001). IICON Construction Group LLC,* Colorado Springs, Colorado, was awarded a $15,179,720 firm-fixed-price contract for construction of a National Guard readiness center. Bids were solicited via the internet with five received. Work will be performed in Fort Carson, Colorado, with an estimated completion date of Aug. 31, 2020. Fiscal 2017 military construction funds in the amount of $15,179,720 were obligated at the time of the award. National Guard Bureau, Arlington, Virginia, is the contracting activity (W912LC-19-C-0001). CORRECTION: The contract announced on Jan. 15, 2019, for $474,084,062 to BAE Systems Land & Armaments LP, York, Pennsylvania, has not been awarded. No award date has been determined at this time. DEFENSE LOGISTICS AGENCY Puerto Rico Apparel Manufacturing (PRAMA) Corp.,** Mayaguez, Puerto Rico, has been awarded a maximum $11,648,229 firm-fixed-price, indefinite-delivery/indefinite-quantity contract for various types of coats and trousers. This was a competitive acquisition with seven responses received. This is a one-year base contract with four one-year option periods. Location of performance is Puerto Rico, with a Jan. 10, 2024, estimated performance completion date. Using military services are Army and Air Force. Type of appropriation is fiscal 2019 through 2024 defense working capital funds. The contracting activity is Defense Logistics Agency Troop Support, Philadelphia, Pennsylvania (SPE1C1-19-D-1127). Alamo Strategic Manufacturing,*** San Antonio, Texas, has been awarded a maximum $8,550,000 firm-fixed-price, indefinite-quantity contract for knee and elbow pads. This was a competitive acquisition with two responses received. This is a one-year base contract with two one-year option periods. Locations of performance are Texas and Puerto Rico, with a Jan. 30, 2020, performance completion date. Using military services are Army, Air Force, and Marine Corps. Type of appropriation is fiscal 2019 through 2020 defense working capital funds. The contracting activity is the Defense Logistics Agency Troop Support, Philadelphia, Pennsylvania. (SPE1C1-19-D-1122). NAVY Gilbane Federal, Concord, California, is awarded a $10,966,383 firm-fixed-price modification to previously awarded contract (N39430-15-D-1634) to decrease the value of the contract for the cleaning, inspection and repair of Fuel Storage Tanks 305, 307, and 308 at Defense Fuel Support Point (DFSP) Tsurumi, Japan. Work on Tanks 305, 307, and 308 is being removed from the contract due to contractor performance problems. After award of this modification, the total cumulative contract value will be $6,426,985. Work will be performed in Tsurumi, Japan, and is expected to be completed by March 2019. Fiscal 2016 defense working capital (Defense Logistics Agency) contract funds in the amount of $10,966,383 are de-obligated on this award and will not expire at the end of the current fiscal year. The Naval Facilities Engineering and Expeditionary Warfare Center, Port Hueneme, California, is the contracting activity. Sikorsky, a Lockheed Martin Co., Stratford, Connecticut, is awarded $7,026,164 for cost-plus-fixed-fee modification P00017 to a previously awarded fixed-price-incentive-firm/cost-plus-fixed-fee contract (N00019-16-C-0048). This modification provides for Automated Logistics Environment software maintenance operating systems and obsolescence avoidance in support of the low rate initial production CH-53K aircraft. The work will be performed in Stratford, Connecticut, and is expected to be completed in October 2021. Fiscal 2018 aircraft procurement (Navy) funds in the amount of $7,026,164 will be obligated at time of award; none of which will expire at the end of the current fiscal year. The Naval Air Systems Command, Patuxent River, Maryland, is the contracting activity. *Small Business **Economically disadvantaged women-owned small business in historically underutilized business zones ***Small disadvantaged business https://dod.defense.gov/News/Contracts/Contract-View/Article/1733689/source/GovDelivery/

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  19 septembre 2018 | International, C4ISR

  Differentiating a port from a shipyard is a new kind of problem for AI

  By: Daniel Cebul It's well known that satellites and other intelligence, surveillance and reconnaissance platforms collect more data than is possible for humans to analyze. To tackle this problem, the Intelligence Advanced Research Projects Activity, or IARPA, conducted the Functional Map of the World (fMoW) TopCoder challenge from July 2017 through February 2018, inviting researchers in industry and academia to develop deep learning algorithms capable of scanning and identifying different classes of objects in satellite imagery. IARPA curated a dataset of 1 million annotated, high-resolution satellite images aggregated using automated algorithms and crowd sourced images for competitors to train their algorithms to classify objects into 63 classes, such as airports, schools, oil wells, shipyards, or ports. Researchers powered their deep learning algorithms by combining large neural networks, known as convolutional neural networks (CNNs), and computers with large amounts of processing power. The result was a network that, when fed massive amounts of training data, can learn to identify and classify various objects from satellite imagery. By combining a number of these networks into what is called an ensemble, the algorithm can judge the results from each CNN to produce a final, improved result that is more robust than any single CNN. This is how a team from Lockheed Martin, led by Mark Pritt, designed their deep learning algorithm for the challenge. Pritt explained to C4ISRNET, that he and his team developed their CNN using machine learning software and framework from online open source software libraries, such as Tensor Flow. Earning a top five finish, the algorithm designed by Pritt's team achieved a total accuracy of 83 percent, and was able to classify 100 objects per second. Pritt said that with fully functioning algorithm, this software could take an image recognition task that takes a human an hour to complete and reduce the process to a few seconds. The team's algorithm excelled at identifying classes with distinctive features, and successfully matched nuclear power plants, tunnel openings, runways, tool booths, and wind farms with accuracies greater than 95 percent, but struggled with more indiscreet classes such as shipyards and ports, hospitals, office buildings, and police stations. “Usually when you develop an algorithm its nice to see where it succeeds, but you actually learn the most where you look at where the algorithm fails or it doesn't do well,” Pritt said. In trying to decipher why the algorithms struggled, Pritt said the competitors suggested that some objects simply don't have any distinguishing features from the point of view of a satellite image for the algorithms to recognize. “Maybe the most important ingredient you need for these new types of algorithm to work is the dataset because these algorithms require a great amount of data to train on,” Pritt explained. “It's kind of analogous to the way a human will learn in childhood how to recognize things. You need lots of examples of what those things are and then you can start to generalize and make your own judgments,” he said. But even with large amounts of training data that is correctly labeled, it is also possible the deep learning technology of today cannot reach the higher levels of intelligence to recognize nuanced differences. For example, Lockheed Martin's algorithm confused shipyards and ports 56 percent of the time. Pritt said that people “look at an image and they can tell that it's a port or a shipyard, they are usually looking at very subtle things such as if there is a ship in dry dock or if there is a certain type of crane present. They are looking for details in the image that are maybe higher level or more complicated than what these deep learning algorithms can do right now.” However, the fact that these algorithms cannot do everything should not dismiss the significant contribution they could provide to the defense and intelligence community. Hakjae Kim, IARPA's program manager for the fMoW challenge, said the benefits of this technology could extend far beyond faster image processing. “I want to look at it more in the perspective that we can do things we weren't able to do before,” Kim said. “Because its technology that we are now able to do x, y and z, there are more applications you can create because with the human power it is just impossible to do before.” Kim and Pritt stressed managing expectations for CNN-based artificial intelligence. “This is a real technology that will work, but it also has limitations. I don't want to express this technology as a magic box that will just solve everything magically,” Kim said. “I don't want the users in the field to get disappointed by the initial delivery of this technology and say 'Oh, this is another technology that was oversold and this is not something we can use," he added. Part of managing our expectations for AI requires recognizing that although intelligence is in the name, this technology does not think and reason like humans. “A lot of the time we think that because we use the term AI, we tend to think these algorithms are like us, they are intelligent like us,” Pritt said. “And in someways they seem to mimic our intelligence, but when they fail we realize ‘Oh, this algorithm doesn't really know anything, [it] doesn't have any common sense.'” So how are IARPA and Lockheed Martin working to improve their algorithms? For IARPA, Kim's team is working on updating and maintaining their dataset to ensure algorithms have the most up to date information to train on, ultimately making the CNN-based algorithms easier to trust. “[S]ubtle changes in the area mess up the brains of the system and that system will give you a totally wrong answer,” Kim explained. “So we have planned to continuously look over the area and make sure the algorithm we are developing and reassessing for the government to test on and use to be robust enough for their application," he furthered. Work is also underway at American universities. Kim described how a team of researchers at Boston University are using the fMoW dataset and tested algorithms to create heat maps that visualize what part of the image algorithms are using to classify objects. They've found that sometimes it is not the object itself, but clues surrounding the object that aid most in classification. For example a “windmill that actually shows a shadow gives a really good indicator of what that object is,” Kim said. “Shadows show a better view of the object. A shadow is casting the side view of the object over on the ground, so [BU's heat map algorithm] actually points out the shadow is really important and the key feature to make the object identified as a windmill.” But don't expect these algorithms to take away the jobs of analysts any time soon. “I think you still need a human doing the important judgments and kind of higher level thinking,” Pritt said. “I don't think AI will take away our jobs and replace humans, but I think what we have to do is figure out how to use them as a tool and how to use them efficiently, and that of course requires understanding what they do well and what they do poorly," he concluded. https://www.c4isrnet.com/intel-geoint/2018/09/18/differentiating-a-port-from-a-shipyard-is-a-new-kind-of-problem-for-ai