| 1. |
- Folke, Mats, et al.
(författare)
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Scheduling support for mixed VoIP and web traffic over HSDPA
- 2007
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Ingår i: 2007 IEEE 65th Vehicular Technology Conference. - Piscataway, NJ : IEEE Communications Society. - 1424402662 ; , s. 814-818
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Konferensbidrag (refereegranskat)abstract
- HSDPA (high-speed downlink packet access), introduced in WCDMA release 5, provides a high-bandwidth shared channel with short transmission time interval (TTI). The short TTI together with appropriate scheduling enable HSDPA to support efficient multiplexing of traffic. We explain the performance of four scheduling algorithms when transmitting a traffic mix consisting of both conversational (VoIP) traffic and background (Web) traffic over the high-speed downlink shared channel (HS-DSCH) of HSDPA. We consider both cell throughput and user satisfaction. The proportional fair (PF), the maximum rate (MR) scheduler and two extended versions of MR, are tested for different VoIP scheduling delay budgets and varying load. To understand the behaviour of the schedulers, we use the ns-2 simulator extended with a model of HS-DSCH to simulate a mixed traffic scenario. Our results show that a scheduler that gradually increases the VoIP priority and considers the user's current possible rate, performs well. A more drastic increase in VoIP priority is however needed when the delay budget is short. Furthermore, attempting to uphold quality for both VoIP and Web traffic makes the system sensitive to overload situations.
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| 2. |
- Huang, Yi, et al.
(författare)
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Application of Kalman learning algorithm multilayer neural network to estimates of ore grades
- 1998
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Ingår i: International Journal of Surface Mining, Reclamation and Environment. - : Informa UK Limited. - 1389-5265 .- 1744-5000. ; 12:1, s. 19-27
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Tidskriftsartikel (refereegranskat)abstract
- We have estimated lead (Pb) and zinc (Zn) grades along boreholes in an ore body based on geophysical logging data by using a Kalman learning algorithm, which is a variety of a back propagation neural network. The data set is from a Swedish mine, the Zinkgruvan Mine. It includes data from seven boreholes. Data on three geophysical logging parameters, gamma-ray, density and susceptibility, and the corresponding Pb and Zn grades obtained from chemical analysis of core samples were available from each borehole. Five of the boreholes were used for training the network and two boreholes were used for testing the successfulness in employing the network results for predictions of Pb and Zn grades. The principal idea of the Kalman learning algorithm is discussed. The minimum error rates of the Pb and Zn grades in the test set are 0.060 and 0.095, respectively. Their corresponding average prediction errors between predicted values from the network and the observed values obtained from the chemical analysis of core samples (expressed as a percentage) are 21.2 and 27.1 %, respectively. The optimum configuration of the neural network is a 4-layer neural network with 3 neurons in the input layer, 7 neurons in the first hidden layer, 3 neurons in the second hidden layer, and 1 neuron in the output layer. The optimum numbers of training epochs for Pb and Zn grades are 600 and 1400, respectively. The results obtained from applications of the Kalman learning algorithm to estimates of Pb and Zn grades in the test set are highly promising. A comparison with results from a conventional back propagation neural network shows that the results obtained from the Kalman learning algorithm are much better. Hence, the Kalman learning algorithm is demonstrated to be more effective than the conventional back propagation algorithm in predicting the ore grades.
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| 3. |
- Huang, Yi, et al.
(författare)
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Application of neural network model for ore boundary delineation based on geophysical logging data
- 1996
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Ingår i: ICNN '96. - Piscataway, NJ : IEEE Communications Society. - 0780332105 ; , s. 2148-2153
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Konferensbidrag (refereegranskat)abstract
- In a mining operation, knowledge regarding the ore boundary is extremely important. Mining cost and ore quality largely depend on this information. The conventional technique to get this information is diamond core drilling. The disadvantages of this technique are that it is very expensive and time consuming. In recent years, geophysical logging has been introduced to the mining industry to get this ore boundary information. However, effective interpretation to delineate the ore boundary from the geophysical logging data is still a problem. In this paper, a back propagation neural network model is applied to delineation of the ore boundary based on borehole 4 geophysical parameters logging data in a Swedish underground mine. Three boreholes geophysical logging data was tested for ore boundary delineation purpose. The result from the neural network model about the ore boundary delineation is encouraging and much better than the existing geophysical logging data interpretation techniques
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| 4. |
- Huang, Yi, et al.
(författare)
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Introduction of neural network system and its applications in rock engineering
- 1998
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Ingår i: Engineering Geology. - 0013-7952 .- 1872-6917. ; 49:3-4, s. 253-260
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Tidskriftsartikel (refereegranskat)abstract
- Neural network systems have a great advantage in dealing with problems in which many factors influence the process and result, and the understanding of this process is poor, and there are experimental data or field data. Most of the these problems occur in rock engineering. This article provides a brief introduction to neural network systems. Problems such as what is a neural network, how it works and what kind of advantages it has are discussed. Several applications in rock engineering are also detailed
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| 5. |
- Huang, Yi, et al.
(författare)
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The use of artificial neural networks for the delineation of boundaries between ore bodies based on geophysical logging data
- 1997
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Ingår i: Mineral resources engineering. - 0950-6098 .- 2047-606X. ; 6:1, s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- An artificial neural network model is applied to the delineation of boundaries between ore bodies based on borehole geophysical logging data, such as gamma-ray, density, neutron and resistivity in a Swedish underground mine, the Renström Mine. The input data set includes the four geophysical logging parameters and the output data to be predicted by the neural network consist of three rock classes, waste rock (rock class 1), semi-ore (rock class 2) and ore (rock class 3). Three boreholes, B33, B34 and B36, each of a length of approximately 40 m, were analyzed. Borehole B33 was divided into 20 sections based on the core log results. The four geophysical logging parameters, initially measured at 0.1 m intervals, were averaged in each section to form the training set for the neural network. The original data from boreholes B33, B34 and B36 were used as the test set. The optimum configuration of the neural network is a 4-layer neural network with 4 neurons in the input layer, 15 neurons in the first hidden layer, 5 neurons in the second hidden layer and 3 neurons in the output layer. The minimum error rate, 0.2160, in the test set, was obtained from training the network over 29,500 epochs. The ability of the neural network to delineate the boundaries between ore bodies from the geophysical logging data is encouraging, and this technique represents a great advantage compared to diamond core drilling and a qualitative and subjective judgement by a geologist in identifying the boundaries between ore bodies from geophysical logging data.
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| 6. |
- Lundevall, Magnus, et al.
(författare)
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Streaming Applications Over HSDPA in Mixed Service Scenarios
- 2004
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Ingår i: Proceedings of the 60th IEEE Vehicular Technology Conference. - 0780385217 ; , s. 841-845
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Konferensbidrag (refereegranskat)abstract
- High-speed downlink packet access (HSDPA) is included in release 5 of the WCDMA specifications to increase downlink capacity and bitrates. This paper considers performance aspects of streaming applications over HSDPA in a mixed streaming and best-effort service scenario. Different scheduling algorithms are evaluated with the aim of providing sufficient quality-of-service for streaming. The simulation results show that reasonable streaming performance can be achieved without service differentiation if a somewhat fair scheduler is used, and that a streaming-aware scheduler further can protect streaming quality-of-service in high load conditions.
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| 7. |
- McCready, Richard G., et al.
(författare)
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Applications of geophysical logging for dilution control in mining
- 1996
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Ingår i: Proceedings of the Symposium on the Applications of Geophysics to Engineering and Environmental Problems. - Wheat Ridge, Colo : Environmental and Engineering Geophysical Society.
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Konferensbidrag (refereegranskat)abstract
- In mining, waste rock dilution occurs when uneconomic rock is mined and processed with economic mineralized ore. A survey of selected stopes from various Noranda Group mines estimated that aggregate dilution and oreloss stood at 20% and 8% respectively in 1994. Various technologies are being investigated by Noranda Group mines in an ongoing effort to minimize dilution and oreloss. The following three potential applications of geophysical logging have been identified for dilution and oreloss reduction through improved orebody delineation. Increasing orebody sampling at the delineation stage to reduce geological dilution: Diamond core drilling, with geological core-logging, is the most commonly used method for orebody delineation. Unfortunately, the many task-intensive steps and expense of this method constrain the amount of samples one can obtain. However, by allotting an optimum percentage of the delineation drilling budget for geophysical loggin of percussion-drilled holes, increased sampling would occur. Since percussion drilling is significantly cheaper than diamond drilling, more delineation holes could be drilled to better define the ore boundary geometry and, ultimately reduce geological dilution and oreloss. Controlling geological dilution at the production stage: In production, thousands of meters of percussion holes are drilled annually for blasting purposes. However, since no core is recovered in this drilling process, information about the ore-waste contact is seldom acquired. Thus, there is the potential to geophysically log production holes to identify the ore-waste contact for optimal blast design with a resulting reduction in dilution and oreloss. Interpreting crosshole geophysics tomograms: By geophysically logging boreholes that were used in a crosshole geophysics survey, relationships can be developed to correlate logged geology and geophysical properties along the boreholes. These relationships could then be used in the interpretation of crosshole tomograms. The objectives are detection of undiscovered lenses and defining "connectivity" by mapping ore contours between delineation drillholes. Through analysis of data acquired during field tests at Louvicourt Mine operated by Aur Resources Inc. in northern Quebec, the potential of these applications of geophysical logging for dilution control in mining are examined.
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