As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 (
There is a general tendency to increase nitrogen level in rice production to insure an increased yield. On the other hand, percentage of ripened grains is getting decreased with such an increased fertilizer level. Decreasing of the percentage is one of the important yield limiting factors. Especially the newly developed rice variety, 'Tongil' is characterized by a relatively low percentage of ripened grains as compared with the other leading varieties. Therefore, these studies were aimed to finding out of some measures for the improvement of ripening in rice. The studies had been carried out in the field and in the phytotron during the period of three years from 1970 to 1972 at the Crop Experiment Station in Suwon. The results obtained from the experiments could be summarized as follows: 1. The spikelet of Tongil was longer in length, more narrow in width, thinner in thickness, smaller in the volume of grains and lighter in grain weight than those of Jinheung. The specific gravity of grain was closely correlated with grain weight and the relationship with thickness, width and length was getting smaller in Jinheung. On the other hand, Tongil showed a different pattern from Jinheung. The relationship of the specific gravity with grain weight was the greatest and followed by that with the width, thickness and length, in order. 2. The distribution of grain weight selected by specific gravity was different from one variety to another. Most of grains of Jinheung were distributed over the specific gravity of 1.12 with its peak at 1.18, but many of grains of Tongil were distributed below 1.12 with its peak at 1.16. The brown/rough rice ratio was sharply declined below the specific gravity of 1.06 in Jinheung, but that of Tongil was not declined from the 1.20 to the 0.96. Accordingly, it seemed to be unfair to make the specific gravity criterion for ripened grains at 1.06 in the Tongil variety. 3. The increasing tendency of grain weight after flowering was different depending on varieties. Generally speaking, rice varieties originated from cold area showed a slow grain weight increase while Tongil was rapid except at lower temperature in late ripening stage. 4. In the late-tillered culms or weak culms, the number of spikelets was small and the percentage of ripened grains was low. Tongil produced more late-tillered culms and had a longer flowering duration especially at lower temperature, resulting in a lower percentage of ripened grains. 5. The leaf blade of Tongil was short, broad and errect, having light receiving status for photosynthesis was better. The photosynthetic activity of Tongil per unit leaf area was higher than that of Jinheung at higher temperature, but lower at lower temperature. 6. Tongil was highly resistant to lodging because of short culm length, and thick lower-internodes. Before flowering, Tongil had a relatively higher amount of sugars, phosphate, silicate, calcium, manganese and magnesium. 7. The number of spikelets of Tongil was much more than that of Jinheung. The negative correlation was observed between the number of spikelets and percentage of ripened grains in Jinheung, but no correlation was found in Tongil grown at higher temperature. Therefore, grain yield was increased with increased number of spikelets in Tongil. Anthesis was not occurred below 21