Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection

Recent studies show edge computing-based road anomaly detection systems which may also conduct data collection simultaneously. However, the edge computers will have small data storage but we need to store the collected audio samples for a long time in order to update existing models or develop a novel method. Therefore, we should consider an approach for efficient storage management methods while preserving high-fidelity audio. A hardware-perspective approach, such as using a low-resolution microphone, is an intuitive way to reduce file size but is not recommended because it fundamentally cuts off high-frequency components. On the other hand, a computational file compression approach that encodes collected high-resolution audio into a compact code should be recommended because it also provides a corresponding decoding method. Motivated by this, we propose a way of simple yet effective pre-trained autoencoder-based data compression method. The pre-trained autoencoder is trained for the purpose of audio super-resolution so it can be utilized to encode or decode any arbitrary sampling rate. Moreover, it will reduce the communication cost for data transmission from the edge to the central server. Via the comparative experiments, we confirm that the zero-shot audio compression and decompression highly preserve anomaly detection performance while enhancing storage and transmission efficiency.Comment: 5 pages, 3 figures, 4 table

Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy

Due to scarcity of anomaly situations in the early manufacturing stage, an unsupervised anomaly detection (UAD) approach is widely adopted which only uses normal samples for training. This approach is based on the assumption that the trained UAD model will accurately reconstruct normal patterns but struggles with unseen anomalous patterns. To enhance the UAD performance, reconstruction-by-inpainting based methods have recently been investigated, especially on the masking strategy of suspected defective regions. However, there are still issues to overcome: 1) time-consuming inference due to multiple masking, 2) output inconsistency by random masking strategy, and 3) inaccurate reconstruction of normal patterns when the masked area is large. Motivated by this, we propose a novel reconstruction-by-inpainting method, dubbed Excision And Recovery (EAR), that features single deterministic masking based on the ImageNet pre-trained DINO-ViT and visual obfuscation for hint-providing. Experimental results on the MVTec AD dataset show that deterministic masking by pre-trained attention effectively cuts out suspected defective regions and resolve the aforementioned issues 1 and 2. Also, hint-providing by mosaicing proves to enhance the UAD performance than emptying those regions by binary masking, thereby overcomes issue 3. Our approach achieves a high UAD performance without any change of the neural network structure. Thus, we suggest that EAR be adopted in various manufacturing industries as a practically deployable solution.Comment: 10 pages, 5 figures, 5 table

Concise Logarithmic Loss Function for Robust Training of Anomaly Detection Model

Recently, deep learning-based algorithms are widely adopted due to the advantage of being able to establish anomaly detection models without or with minimal domain knowledge of the task. Instead, to train the artificial neural network more stable, it should be better to define the appropriate neural network structure or the loss function. For the training anomaly detection model, the mean squared error (MSE) function is adopted widely. On the other hand, the novel loss function, logarithmic mean squared error (LMSE), is proposed in this paper to train the neural network more stable. This study covers a variety of comparisons from mathematical comparisons, visualization in the differential domain for backpropagation, loss convergence in the training process, and anomaly detection performance. In an overall view, LMSE is superior to the existing MSE function in terms of strongness of loss convergence, anomaly detection performance. The LMSE function is expected to be applicable for training not only the anomaly detection model but also the general generative neural network.Comment: 6 pages, 3 figures, 2 table

Fast Adaptive RNN Encoder–Decoder for Anomaly Detection in SMD Assembly Machine

Surface Mounted Device (SMD) assembly machine manufactures various products on a flexible manufacturing line. An anomaly detection model that can adapt to the various manufacturing environments very fast is required. In this paper, we proposed a fast adaptive anomaly detection model based on a Recurrent Neural Network (RNN) Encoder–Decoder with operating machine sounds. RNN Encoder–Decoder has a structure very similar to Auto-Encoder (AE), but the former has significantly reduced parameters compared to the latter because of its rolled structure. Thus, the RNN Encoder–Decoder only requires a short training process for fast adaptation. The anomaly detection model decides abnormality based on Euclidean distance between generated sequences and observed sequence from machine sounds. Experimental evaluation was conducted on a set of dataset from the SMD assembly machine. Results showed cutting-edge performance with fast adaptation

Fast Adaptive RNN Encoder–Decoder for Anomaly Detection in SMD Assembly Machine

Surface Mounted Device (SMD) assembly machine manufactures various products on a flexible manufacturing line. An anomaly detection model that can adapt to the various manufacturing environments very fast is required. In this paper, we proposed a fast adaptive anomaly detection model based on a Recurrent Neural Network (RNN) Encoder–Decoder with operating machine sounds. RNN Encoder–Decoder has a structure very similar to Auto-Encoder (AE), but the former has significantly reduced parameters compared to the latter because of its rolled structure. Thus, the RNN Encoder–Decoder only requires a short training process for fast adaptation. The anomaly detection model decides abnormality based on Euclidean distance between generated sequences and observed sequence from machine sounds. Experimental evaluation was conducted on a set of dataset from the SMD assembly machine. Results showed cutting-edge performance with fast adaptation

Complications of fluid overload during hysteroscopic surgery: cardiomyopathy and epistaxis - A case report -

Background Hysteroscopic surgery has been used in various gynecological fields. However, massive fluid overload can occur as a complication due to persistent infusion of media for uterine cavity distension. We present the case of a woman who developed cardiomyopathy with pulmonary edema and epistaxis during hysteroscopic surgery. Case A 76-year-old female underwent hysteroscopic septectomy. She manifested abrupt, active nasal bleeding and regurgitation in the intravenous line. Heart rate, SpO2, and PETCO2 decreased from 55 beats/min to 29 beats/min, from 100% to 56%, and from 31 mmHg to 9 mmHg, respectively. After the operation, brain CT showed bilateral prominent superior ophthalmic vein dilation. Echocardiography showed left ventricle apical ballooning and global hypokinesia. The patient recovered after two days of conservative management, with no sequelae. Conclusions Although hysteroscopic surgery is a simple procedure, careful monitoring is necessary to prevent complications from absorption of fluid distending media during the procedure