Energy saving at Syowa and Mizuho Stations

"At Syowa Station, which was opened in 1957, two diesel electric generators were installed, one of which was always operated as the main energy source. The electric capacity of each generator has been increased from 20 kVA to 110 kVA in accordance with the expansion of the station. In order to save fuel consumption, the authors have developed some waste heat recovery systems of the diesel engines. By fully utilizing the waste heat of diesel engines, i.e., their exhaust-gas energy and coolant energy, cold and hot water was made from ice or snow even in winter. The hot and cold water was supplied to the living quarters through insulated water pipes. The hot water was also supplied for bathing and heating of apartments of the buildings. At Mizuho Station, which was opened in 1970, a system for recovering coolant heat of a diesel electric generator was installed. The cold and hot water is made by the similar system. The hot water is supplied to a bathtub and to a fan-coil unit in a trench living room. The heating by utilizing the waste coolant can ensure the safety of the personnel living in the trench room against fire, contamination by CO, CO_2 and lack of oxygen. In this report, the technical problems and experiences on waste heat recovering, especially on exhaust-gas heat exchangers are described.

Novel Approximate Statistical Algorithm for Large Complex Datasets

In the field of pattern recognition, principal component analysis (PCA) is one of the most well-known feature extraction methods for reducing the dimensionality of high-dimensional datasets. Simple-PCA (SPCA), which is a faster version of PCA, performs effectively with iterative operated learning. However, SPCA might not be efficient when input data are distributed in a complex manner because it learns without using the class information in the dataset. Thus, SPCA cannot be said to be optimal from the perspective of feature extraction for classification. In this study, we propose a new learning algorithm that uses the class information in the dataset. Eigenvectors spanning the eigenspace of the dataset are produced by calculating the data variations within each class. We present our proposed algorithm and discuss the results of our experiments that used UCI datasets to compare SPCA and our proposed algorithm

Adaptive data rate control TDMA systems as a rain attenuation compensation technique

Rainfall attenuation has a severe effect on signal strength and impairs communication links for future mobile and personal satellite communications using Ka-band and millimeter wave frequencies. As rain attenuation compensation techniques, several methods such as uplink power control, site diversity, and adaptive control of data rate or forward error correction have been proposed. In this paper, we propose a TDMA system that can compensate rain attenuation by adaptive control of transmission rates. To evaluate the performance of this TDMA terminal, we carried out three types of experiments: experiments using a Japanese CS-3 satellite with Ka-band transponders, in house IF loop-back experiments, and computer simulations. Experimental results show that this TDMA system has advantages over the conventional constant-rate TDMA systems, as resource sharing technique, in both bit error rate and total TDMA burst lengths required for transmitting given information