Internal short circuit that occurs inside lithium-ion batteries is known as one of the causes of thermal runaway. If micro-internal short circuits can be detected, the anomalies at very early stage can be known, and it will contribute to improved safety when using lithium-ion batteries. The purpose of this study is to fabricate a software architecture that can detect micro-internal short circuits of lithium-ion batteries during flight with a view to application to electric aircraft that require high safety. In this research, we first prepared a new lithium-ion battery and another lithium-ion battery of the same model that was intentionally deteriorated to make it easy to cause an internal short circuit. Next, we designed four features which denote a characteristic voltage behavior when the micro-internal short circuit occurs. A large feature value was obtained from the deteriorated battery, while such a value could not be obtained from the new battery. Therefore, we considered new batteries to be normal specimens, and tried to detect the abnormality of the deteriorated battery by k nearest neighborhood method. As a result, it was shown that micro-internal short circuits of lithium-ion batteries can be detected based on the features describing the behavior of the abnormal voltage change by the micro-internal short circuit.
The purpose of this paper is to understand tourism behavior in Yokohama by analyzing the tourist’s behavior on weekends and holidays in April and May of 2019 and 2022 using GPS data of visitors in Nishi and Naka wards of Yokohama City. The area was divided into 18 areas by tourist area, the transition data between areas were generated from the tourist movement, and these data were analyzed by the k-medoids method and Asymmetric Cluster analysis.
Analysis of the k-medoids method provided cluster structures centered on adjacent areas for both 2019 and 2022. In the asymmetric cluster analysis, clusters were constructed by relatively close areas in the anaylsis of the 2019 data, but in the analysis of the 2022 data, a characteristic cluster consisting of a wide range of areas from the Minato Mirai area to the Yamashita Park area centered on Shinko area appeared. It is shown that these results show differences in tourism behavior between the two sets of data. In addition, because of the analysis that considers asymmetry, the results allow for interpretation based on the directionality of tourisum behavior, which takes into account the inflows and outflows of the areas.
Most of the existing stylistic features of Japanese writings used to attribute authorship are based on various linguistic units that constitute sentence elements like characters and words. However, attempts to convert the structural characteristics of the sentence into stylometric features are limited and not quite effective in distinguishing authorship. Following the earlier research, we represented Japanese sentences by dendrograms branched over the predicative clause. We defined the root nodes and clauses sprouting directly from those root nodes as “Nucleus Bunsetsu” and then proposed a series of new stylometric features called the NBSs. To examine the effectiveness of our proposal, we compared the attributional accuracies of the NBSs and Phrase Pattern, a clause-based stylometric feature, over a corpus containing the works of ten contemporary authors belonging to two literary genres, i.e., the novel and essay. The results revealed that, although our approach was narrowly outperformed by Phrase Pattern when there were two suspected authors, it turned the tables on opponent by 2% when there were ten candidates. Therefore, we concluded that the dependency structure-derived stylometric feature is sufficiently effective for authorship attribution and can reflect a new attempt to capture authorial idiosyncrasies, which might be overlooked by the existing ones.