鉱業都市から"コンパクトシティ"へ：中国西北部のカラマイ市における都市空間形態の変容に関する考察

Tohoku University博士（工学）要約のみthesi

Contract Choice, Moral Hazard, and Performance Evaluation: Evidence from Online Labor Markets

Due to the spatial and temporal separations between clients and freelancers, online labor markets (OLMs) are particularly susceptible to issues related to information asymmetry. Based on the economics of information, we hypothesize that the choice of contract type—i.e., between the fixed-priced (FP) contract and the time-and-materials (TM) contract—has important implications for curbing moral hazard during contract execution, and therefore will influence the client’s perceived contractual performance upon project completion. We test the predictions by assembling a dataset of data analytics projects completed by freelancers on Upwork, the largest online freelancing platform. We find that, consistent with our hypothesis, freelancers under a TM contract receive significantly lower performance ratings by their clients on average compared to those under an FP contract. Interestingly, we also find that the level of expertise required for a project moderates the effect of contract choice on client satisfaction; the negative impact of a TM contract is smaller (i.e., less negative) when a project requires intermediate-level or expert-level skills. Our study offers useful insights into an important institutional determinant of contractual performance evaluation, which has profound implications for freelancers’ reputations in OLMs

Condition monitoring of wind turbines based on extreme learning machine

Wind turbines have been widely installed in many areas, especially in remote locations on land or offshore. Routine inspection and maintenance of wind turbines has become a challenge in order to improve reliability and reduce the energy of cost; thus adopting an efficient condition monitoring approach of wind turbines is desirable. This paper adopts extreme learning machine (ELM) algorithms to achieve condition monitoring of wind turbines based on a model-based condition monitoring approach. Compared with the traditional gradient-based training algorithm widely used in the single-hidden layer feed forward neural network, ELM can randomly choose the input weights and hidden biases and need not be tuned in the training process. Therefore, ELM algorithm can dramatically reduce learning time. Models are identified using supervisory control and data acquisition (SCADA) data acquired from an operational wind farm, which contains data of the temperature of gearbox oil sump, gearbox oil exchange and generator winding. The results show that the proposed method can efficiently identify faults of wind turbines