The network coding can spread a single original error over the whole network. The simulation shows that the propagated error mostly all the time pollute just 100% of the received packets at the sink if Hamming distance is adopted. If subspace codes are adopted, usually the propagated error will not pollute 100% of the received packets in the sense of subspace distance. However, it also usually pollutes 90% of received packets which is a high error ratio. Even if the rank code and the subspace code are adopted, these existing schemes based on traditional block codes can correct corrupted errors no more than C/2 because of the limitation of the block coding where C is the max flow min cut. It is an agent to find a dense error correction method in random network coding. List decoding of subspace codes can correct $$ \frac{C}{k}\hbox{-} 1 $$ errors where k is the size of information. When $$ \frac{C}{k} $$ is big, many errors in the sense of subspace distance can be corrected. However, the solution of list decoding is not unique. John Wright proposed a dense error correction technique based on L1 minimization, which can recover nearly 100% of the corrupted observations. In our proposal, the original packets are coded with John Wright's coding matrix, and then, the coded message is coded again with subspace codes. In the sink, the decoding procedures about list decoding of subspace codes and John Wright's scheme are performed. At last, the unique solution is achieved even though there are dense propagated errors in random network coding.
The market competition of beer become more and more severe day by day and customers' request for the quality of beer become higher and higher,the technique of beer filtration develops rapidly,manifold new patterns filtration technique have been applied in the manufacture.The insight have been reviewed the beer filtration techniques which applied in brewhouse and forecasted the foreground of beer filtration in China.
Network storages are used widely in many fields of our society. But their testing performances are not as expected. Most of the tools being used to test the performance of network storage comes from the tools used to test the traditional storage system because the storage devices connected to the net act as the local storage devices. So these tools inevitably miss the effects exerted by the networks which connect the hosts to the storage devices. For example, these tools can hardly generate requests which exceed the maximum loading of storage devices. In this paper, we propose one new method to test the performance of network storage which can easily generate requests that exceed the maximum loading of storage devices. In this method, the test program sends the requests by fixed frequency and the initiator will not be restrained by the targeter when the quantity of requests is close to the maximum load. The load simulated by the new method is more like the load in the real world, especially when the load is very high.
Network storages are used widely in many fields of our society. But their testing performances are not as expected. Most of the tools being used to test the performance of network storage comes from the tools used to test the traditional storage system because the storage devices connected to the net act as the local storage devices. So these tools inevitably miss the effects exerted by the networks which connect the hosts to the storage devices. For example, these tools can hardly generate requests which exceed the maximum loading of storage devices. In this paper, we propose one new method to test the performance of network storage which can easily generate requests that exceed the maximum loading of storage devices. In this method, the test program sends the requests by fixed frequency and the initiator will not be restrained by the targeter when the quantity of requests is close to the maximum load. The load simulated by the new method is more like the load in the real world, especially when the load is very high.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)