• Resources Recycling
• /
• v.8 no.4
• /
• pp.3-10
• /
• 1999

An experimental study for manufacturing lightweight aggregates from sewage sludge ash of sewage treatment facilities was carned out in senses of sludge disposal and utilization. In case of SA alone, aggregates with S.G. 1.67 was obtained at sintering temperature of $1250^{\circ}C$, and heating rate affects little to volume expansion rate and volume expansion rate was decreased more or less by heating time. In case of adding 10 wt% of RFCC to SA, lightweight aggregates with S.G. 1.07 was obtained and volume expansion rate was increased by heating time. The optimum adding amounts of RFCC to manufacture lightweight aggregates was 10wt% and the condition that shows maximum volume expansion rate was $20^{\circ}C/min$ of heating rate, $1250^{\circ}C$ of sintering temperature and 50 mmutes of heating time. According to adding amounts of RFCC, Compressive strength of sample results in 30 wt% RFCC>SA>20 wt% RFCC>10 wt% RFCC.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.2
• /
• pp.389-392
• /
• 2017

Previously, PCC and RFCC were proposed for the network coding possibility test of flow-intersecting nodes in a multi-hop wireless network. PCC works correctly only if there is only one intersecting node. RFCC solves this problem by defining the network coding conditions with considering decoding possibility at intermediate nodes. However, this may increase decoding possibility test overhead and coding operations at intermediate nodes. In this paper, we define DCC which can decrease this overhead by allowing decoding only at destinations. We analyze the performance of DCC by simulations.

• Journal of Information Processing Systems
• /
• v.13 no.6
• /
• pp.1467-1486
• /
• 2017

Fuzzy co-clustering is sensitive to noise data. To overcome this noise sensitivity defect, possibilistic clustering relaxes the constraints in FCM-type fuzzy (co-)clustering. In this paper, we introduce a new possibilistic fuzzy co-clustering algorithm based on information bottleneck (ibPFCC). This algorithm combines fuzzy co-clustering and possibilistic clustering, and formulates an objective function which includes a distance function that employs information bottleneck theory to measure the distance between feature data point and feature cluster centroid. Many experiments were conducted on three datasets and one artificial dataset. Experimental results show that ibPFCC is better than such prominent fuzzy (co-)clustering algorithms as FCM, FCCM, RFCC and FCCI, in terms of accuracy and robustness.