A New Fault Diagnosis Method Based on Improved DQN for Cutting Tools

The practices of fault diagnosis present challenges in obtaining sensitive fault characteristics of tool system leading to poor fault diagnosis accuracy and jeopardizing equipment safety. To address above problems, an improved Deep Q Network (DQN) deep reinforcement learning fault diagnosis method is proposed. The new method utilizes a one-dimensional wide convolutional neural network to fit the Q network, with one-dimensional vibration signals and fault types serving as action ensemble inputs. Meanwhile, the ε-greedy strategy guides decision action and feedback reward is employed. The agent in the method uses time difference error (TD-error) priority experience playback enhancing stability and convergence. The algorithm continuously interacts with the decision to maximize the reward and reach to the optimal strategy fault diagnosis results. The model is applied to the cutting tools worn test bench dataset and achieves an accuracy of 99.08%, which can be used for fast and effective fault diagnosis. The results demonstrate the high fault diagnosis accuracy and generality of the improved DQN model, providing potential for enhancing equipment safety and efficiency.</p

TipScreener: A Framework for Mining Tips for Online Review Readers

User-generated content explodes in popularity daily on e-commerce platforms. It is crucial for platform manipulators to sort out online reviews with repeatedly expressed opinions and a large number of irrelevant topics in order to reduce the information processing burden on review readers. This study proposes a framework named TipScreener that generates a set of useful sentences that cover all of the information of features of a business. Called tips in this work, the sentences are selected from the reviews in their original, unaltered form. Firstly, we identify information tokens of the business. Second, we filter review sentences that contain no tokens and remove duplicates. We then use a convolutional neural network to filter uninformative sentences. Next, we find the tip set with the smallest cardinality that contains all off the tokens, taking opinion words into account. The sentences of the tip set contain a full range of information and have a very low repetition rate. Our work contributes to the work of online review organizing. Review operators of e-commerce platforms can adopt tips generated by TipScreener to facilitate decision makings of review readers. The convolutional neural network that classifies sentences into two classes also enriches deep learning studies on text classification

TipScreener: A Framework for Mining Tips for Online Review Readers

User-generated content explodes in popularity daily on e-commerce platforms. It is crucial for platform manipulators to sort out online reviews with repeatedly expressed opinions and a large number of irrelevant topics in order to reduce the information processing burden on review readers. This study proposes a framework named TipScreener that generates a set of useful sentences that cover all of the information of features of a business. Called tips in this work, the sentences are selected from the reviews in their original, unaltered form. Firstly, we identify information tokens of the business. Second, we filter review sentences that contain no tokens and remove duplicates. We then use a convolutional neural network to filter uninformative sentences. Next, we find the tip set with the smallest cardinality that contains all off the tokens, taking opinion words into account. The sentences of the tip set contain a full range of information and have a very low repetition rate. Our work contributes to the work of online review organizing. Review operators of e-commerce platforms can adopt tips generated by TipScreener to facilitate decision makings of review readers. The convolutional neural network that classifies sentences into two classes also enriches deep learning studies on text classification

Research on Service-Driven Benign Market with Platform Subsidy Strategy

The benign consumption of two-sided markets and the quality improvement of the supply side is the core of the sustainable development of platform ecology. This paper discusses how the platform uses personalized service values to influence the decision making of manufacturers and consumers, thus improving the health development of the platform ecosystem. By constructing the vertical differentiation model, we find that, different from the unified pricing strategy in the benchmark market, manufacturers in the platform market can implement personalized pricing, according to the different types of consumers’ quality preferences. When the platform service value is less than the product cost difference between manufacturers, low-quality manufacturers may benefit from the platform. Meanwhile, when the platform service value is greater than the product cost difference between manufacturers, the lemon market may appear and platforms should set the differentiated subsidy strategy according to the type of market consumers; this is a dominant strategy. In addition, when the number of consumers with low-quality demand in the market is large, the platform’s subsidies for high-quality products to consumers will guide consumers to buy high-quality products; this will not only promote the development of the benign market, but also improve the platform’s revenue. Finally, the sensitivity analysis shows that the platform service value has a U-shaped impact on the platform revenue and an inverted U-shaped impact on the manufacturers’ revenues

An Analysis Framework to Reveal Automobile Users’ Preferences from Online User-Generated Content

This work attempts to develop a novel framework to reveal the preferences of Chinese car users from online user-generated content (UGC) and guides automotive companies to allocate resources reasonably for sustainable design and improve existing product or service attributes. Specifically, a novel unsupervised word-boundary-identified algorithm for the Chinese language is used to extract domain professional feature words, and a set of sentiment scoring rules is constructed. By matching feature-sentiment word pairs, we calculate car users’ satisfaction with different attributes based on the rules and weigh the importance of attributes using the TF-IDF method, thus constructing an importance-satisfaction gap analysis (ISGA) model. Finally, a case study is used to realize the framework evaluation and analysis of the twenty top-mentioned attributes of a small-sized sedan, and the dynamic ISGA-time model is constructed to analyze the changing trend of the importance of user demand and satisfaction. The results show the priority of resource allocation/adjustment. Fuel consumption and driving experience urgently need resource input and management

Social Welfare Analysis under Different Levels of Consumers&rsquo; Privacy Regulation

With the rapid development of information technology, digital platforms can collect, utilize, and share large amounts of specific information of consumers. However, these behaviors may endanger information security, thus causing privacy concerns among consumers. Considering the information sharing among firms, this paper constructs a two-period duopoly price competition Hotelling model, and gives insight into the impact of three different levels of privacy regulations on industry profit, consumer surplus, and social welfare. The results show that strong privacy protection does not necessarily make consumers better off, and weak privacy protection does not necessarily hurt consumers. Information sharing among firms will lead to strong competitive effects, which will prompt firms to lower the price for new customers, thus damaging the profits of firms, and making consumers&rsquo; surplus higher. The level of social welfare under different privacy regulations depends on consumers&rsquo; product-privacy preference, and the cost of information coordination among firms. With the cost of information coordination among firms increasing, it is only in areas where consumers have greater privacy preferences that social welfare may be optimal under the weak regulation

Competitive Price-Quality Strategy of Platforms under User Privacy Concerns

The behavior-based discrimination price model (BBPD) needs to collect a large amount of user information, which would spark user privacy concerns. However, the literature on BBPD typically overlooks consumer privacy concerns. Additionally, most of the existing research provides some insights from the perspective of traditional privacy protection measures, but seldom discusses the role of quality discrimination in alleviating users&rsquo; privacy concerns. By establishing a Hotelling duopoly model of two-period price-quality competition, this paper explores the impact of quality discrimination on industry profits, user surplus, and social welfare under user privacy concerns. The results show that, with the increase of user privacy cost, given weak market competition intensity, quality discrimination can increase users&rsquo; surplus and social welfare, thereby alleviating users&rsquo; privacy concerns. We then discuss the managerial implications for alleviating consumer privacy concerns. In addition, we take Airbnb as an example to provide practical implications

Research on the Impact of Online Promotions on Consumers’ Impulsive Online Shopping Intentions

Online shopping has developed rapidly, but recently, the sales of some online stores have suffered due to the decrease in people’s income caused by the epidemic. How to grasp the psychology and behavior of consumers and formulate effective marketing strategies is important for increasing sales. This paper puts forward a research model and eight hypotheses based on the research on the promotion situation and the types of products promoted on consumers’ impulse shopping, and uses regression analysis, t-test, stepwise regression and analysis of variance to conduct data analysis. The results show that online promotion has a significant impact on consumers’ willingness, and the anticipated regrets in different directions have totally different effect on willingness; the type of product promoted, and the impulsive characteristics of consumers play a moderating role; online promotion affects consumers’ impulsive online shopping intentions through the intermediary effect of expected regret. The influence of anticipated regrets on impulsive online shopping intention is proposed creatively, and the results also provide e-commerce merchants and customers with new insights in managing and treating online promotions. Managerial implications like controlling the duration of promotions and the number of preferential goods are put forward based on our analysis