Fault diagnosis plays a significant role in the intelligent maintenance of industrial processes. Although there are numerous deep learning methods designed for fault diagnosis, certain challenges remain difficult to address in real-world industrial scenarios. These challenges include ensuring high- precision fault detection while maintaining industrial privacy in the presence of sensitive data, as well as achieving accurate detection when fault samples are limited. This paper proposes an industrial data generation framework using diffusion and flow- based models. The proposed approach learns the distribution of the original data to generate synthetic samples with similar characteristics, effectively resolving the dual challenges of data sensitivity and scarcity. Experiments on the Tennessee Eastman dataset demonstrate that our approach outperforms existing methods in fault diagnosis performance.
This Participatory Action Design Research (PADR) study explores how persuasive technologies and the Internet of Things (IoT) can be used to encourage participation in recycling activities. By involving primary school students in the design process of a sociotechnical solution through participatory methods, we aim to create intelligent and persuasive applications that effectively promote sustainable behaviors in the school environment. To address the objectives of this investigation, the PADR methodology was adapted to the context of persuasive technologies by incorporating some critical activities, such as defining target behaviors. We argue that applying a participatory action design research approach involving users in all the stages of the design process of persuasive technologies for sustainability can lead to engaging and intuitive solutions tailored to the specific needs of users in real-world contexts.
WiFi probes are crucial tools in applications such as crowd analysis and traffic monitoring. Due to indiscriminate sniffing of WiFi packets in the environment, probes generate a large amount of irrelevant data, increasing server-side processing overhead and presenting challenges for efficient data collection and real-time information processing. Moreover, a single wired transmission mode significantly limits the application scope and deployment efficiency of probes. In response, this paper presents a lightweight WiFi probe system combining a dual transmission mode with device classification capability. First, the system hardware, composed of a single STM32 and multiple ESP8266 modules, enables both wired and wireless data transmission modes through embedded programming. Second, a lightweight logistic and decision fusion model (LLDFM) is proposed, utilizing a time window and device library dynamic management mechanism to achieve real-time classification of fixed/temporary WiFi devices, which can effectively distinguish and discard application-irrelevant fixed device data. Finally, a server web platform was developed with real-time data sniffing, storage, multi-probe monitoring, and management functions to evaluate the system's practical performance. Results from extensive real-world experiments demonstrate an average fault-free operational time of over 168 hours, a fixed/temporary WiFi device classification accuracy of 97.19 %, and the ability to filter 30.45 % of irrelevant data, reducing the corresponding data transmission volume. These findings confirm the proposed system's reliability, efficiency, and superiority.
In recent years, the development of Decision Support Systems (DSS) has played an instrumental role in the advancement of offshore renewable energy projects, particularly within the blue energy sector. Notwithstanding the technological advancements that have been made, the acceleration of such projects continues to be impeded by significant obstacles related to stakeholder engagement, feasibility assessment, and policy compliance. The objective of this study is to propose a design for a DSS for accelerating the construction of blue offshore energy platforms. This is to address the aforementioned challenges by integrating insights from stakeholder feedback and innovation trends. A participatory action study was conducted through a workshop with a diverse group of experts (n=20), including policymakers, practitioners, researchers, and public entities involved in offshore energy projects. The evaluation facilitated the determination of the DSS’ s efficacy in addressing user requirements and the identification of areas for enhancement. This study proposes a model for integrating stakeholder insights into technological solutions for offshore energy installations, thus offers significant contributions to the domain of sustainable blue energy development.
In the field of knowledge graph completion, traditional methods mainly rely on Knowledge Graph Embedding (KGE), which maps entities and relations into a low-dimensional vector space to predict missing links in the knowledge graph. However, as the embedding dimensions increase, the performance improvement is limited, while the computational resources and storage requirements significantly grow, making lightweight re-search for low-resource challenging. To address this issue, this paper proposes an innovative dual stage distillation that combines traditional knowledge distillation with self-distillation, dividing the iterative training of the model into a continuous sequence of iterations, with each pair forming a dual stage distillation unit, allowing the model to accumulate and optimize knowledge across continuous training periods. In addition, we propose a dynamic sample weight allocation mechanism, adjusting the weights of samples based on their quality, ensuring that high-quality sam-ples receive more attention and reducing the negative impact of low-quality samples. A series of experiments demonstrate that our proposed method outperforms existing baseline models on most metrics, especially in terms of accuracy metric (MRR), where we achieved state-of-the-art results.
