Bearing Fault Diagnosis via Incremental Learning Based on the Repeated Replay Using Memory Indexing (R-REMIND) Method

In recent years, deep-learning schemes have been widely and successfully used to diagnose bearing faults. However, as operating conditions change, the distribution of new data may differ from that of previously learned data. Training using only old data cannot guarantee good performance when handling new data, and vice versa. Here, we present an incremental learning scheme based on the Repeated Replay using Memory Indexing (R-REMIND) method for bearing fault diagnosis. R-REMIND can learn new information under various working conditions while retaining older information. First, we use a feature extraction network similar to the Inception-v4 neural network to collect bearing vibration data. Second, we encode the features by product quantization and store the features in indices. Finally, the parameters of the feature extraction and classification networks are updated using real and reconstructed features, and the model did not forget old information. The experiment results show that the R-REMIND model exhibits continuous learning ability with no catastrophic forgetting during sequential tasks

Bearing Fault Diagnosis via Incremental Learning Based on the Repeated Replay Using Memory Indexing (R-REMIND) Method

In recent years, deep-learning schemes have been widely and successfully used to diagnose bearing faults. However, as operating conditions change, the distribution of new data may differ from that of previously learned data. Training using only old data cannot guarantee good performance when handling new data, and vice versa. Here, we present an incremental learning scheme based on the Repeated Replay using Memory Indexing (R-REMIND) method for bearing fault diagnosis. R-REMIND can learn new information under various working conditions while retaining older information. First, we use a feature extraction network similar to the Inception-v4 neural network to collect bearing vibration data. Second, we encode the features by product quantization and store the features in indices. Finally, the parameters of the feature extraction and classification networks are updated using real and reconstructed features, and the model did not forget old information. The experiment results show that the R-REMIND model exhibits continuous learning ability with no catastrophic forgetting during sequential tasks

A Jpeg Image Coding Scheme For Wireless Multimedia Sensor Networks

With resource-constrained wireless multimedia sensor networks, image coding and transmission must respect the trade-off among energy consumption, compression ratio and image quality. Compression of videosurveillance image sequences collected by a wireless multimedia sensor network is studied. A low-complexity image compression scheme based on change detection and adapted JPEG is proposed. For adaptation to the limited capacity of store and forward, change detection algorithm is used to locate the region of interest and cut down the data for transmission by only transmitting the region of interest. For adaptation to the limited computational capability, DCT and quantization of JPEG is optimized to reduce the computation complexity. Computation complexity analysis and simulation results indicate that the proposed image compression scheme effectively reduces the data traffic and energy consumption of computation under the required image quality

Mussel byssus-inspired dual-functionalization of zirconia dental implants for improved bone integration

Zirconia faces challenges in dental implant applications due to its inherent biological inertness, which compromises osseointegration, a critical factor for the long-term success of implants that rely heavily on specific cell adhesion and enhanced osteogenic activity. Here, we fabricated a dual-functional coating that incorporates strontium ions, aimed at enhancing osteogenic activity, along with an integrin-targeting sequence to improve cell adhesion by mussel byssus-inspired surface chemistry. The results indicated that although the integrin-targeting sequence at the interface solely enhances osteoblast adhesion without directly increasing osteogenic activity, its synergistic interaction with the continuously released strontium ions from the coating, as compared to the release of strontium ions alone, significantly enhances the overall osteogenic effect. More importantly, compared to traditional polydopamine surface chemistry, the coating surface is enriched with amino groups capable of undergoing various chemical reactions and exhibits enhanced stability and aesthetic appeal. Therefore, the synergistic interplay between strontium and the functionally customizable surface offers considerable potential to improve the success of zirconia implantation