Mobile Crowdsensing (MCS) is a new data acquisition method that has emerged with the proliferation of smart mobile devices. With the expanding scale of urban sensing, the locations of tasks and users become critical information, which plays a significant role in crowd-sensing and task scheduling areas. Tasks in areas with a high concentration of users can be completed quickly, whereas tasks in sparsely populated areas are challenging to accomplish. To address this issue, existing research has primarily focused on task assignment to designated users, assuming that users' motivations are rational, while neglecting the impact of psychological factors on their motivations. Therefore, we propose an incentive mechanism based on prospect theory, analyzing the decisions users might make under irrationality and then adjusting corresponding rewards to influence user decisions. This paper transforms the problem of maximizing the data value in crowdsensing into an ordered submodular function model. Our proposed incentive mechanism consists of three components: User Decision-Making, User Selection, and Payment Determination. In the User Decision-Making phase, users calculate the prospect value based on the auction results from the previous round to make decisions. In the User Selection phase, users are chosen based on marginal value. In the Payment Determination phase, rewards for winning users are designed based on the ordered submodular model. The platform provides auction results as a reference for the next round. In the experimental section, we demonstrate that the incentive mechanism can enhance the platform's value.

Fault diagnosis with formal languages can be performed in an interpretable way. However, the traditional formal languages cannot deal with noisy environments. Additionally, finding the optimal formal language for fault diagnosis is still a challenge due to the sparse reward issue. This article presents a novel method to find formal languages, written with signal spectral logic (SSL), to describe the fault behaviors among frequency domain for fault diagnosis. The formal language defined by SSL is robust to noise, acts as the fault diagnoser, and provides interpretabilities for human operators. Moreover, the fault diagnoser construction procedure has been formulated as a language generation process and an adversarial training technique is used to find the optimal formal language and avoid sparse reward issue existing in language generation problems. Some experiments with real rolling element bearing data and simulated signals demonstrate that our method is able to find formal languages to diagnose faults efficiently and accurately under noisy environments.