In this study, we propose a method for contactless anomaly detection using event cameras, which are well -suited for industrial environments due to their high temporal resolution and robustness to noise. Our approach reconstructs continuous mechanical vibrations from sparse event data and applies CNN-based classification to spectrogram representations. Experiments on DC motors confirmed that (1) periodic and frequency structures of vibrations can be clearly reconstructed, and (2) anomalies can be accurately detected and classified. This method demonstrates the potential of event-based vision for low-cost, real-time condition monitoring.

In the process of garment production, creating sewing patterns which serve as design blueprints for clothing, from fashion sketches is a highly complex task that requires extensive experience and knowledge from garment makers. In this paper, we propose a method for generating sewing patterns from clothing images by constructing a dataset based on sewing patterns used in actual dressmaking.In addition, we train a pattern image generation model using Generative Adversarial Networks and report that In some cases, it was possible to generate sewing patterns that reflected the shape of the clothing.

The previous paper reported the development and experimental results of a linear motion ball guide with an eight-grooved structure (8-grooved LMBG). The 8-grooved LMBG achieved higher motion accuracy by reducing waving amplitude. Consequently, the 8-grooved LMBG has satisfied the market demand for higher accuracy. However, it is difficult for the 8-grooved LMBG to replace ordinary LMBGs used in already-designed machines due to its larger dimensions compared to ISO standard dimensions (ISO dimensions). Therefore, a new 8-grooved LMBG that meets ISO dimensions is needed. In order to meet this demand, a more compact design for the 8-grooved LMBG is required. Thus, the following three design methods have been considered. First, the ball size has been reduced. Second, the ball layout has been staggered to decrease the distance between neighboring rows. Third, the circulating trajectory of balls and cages on the turning part has been designed with two different radii; the conventional one has only one radius. Based on these design methods, a new 8-grooved LMBG that meets ISO dimensions has been developed. This paper reports the detailed design of the new 8-grooved LMBG and experimental results using a prototype.

This paper proposes a method for interpolating missing images in large-scale datasets used for training Vision-Language Models (VLMs). Recent large-scale datasets are often distributed not by hosting the image files directly on servers, but by providing CSV files that contain download links and the corresponding text for each image. As a result, many images become unavailable due to broken links, making it difficult to reproduce the VLM performance reported in previous studies. To address this issue, we propose an interpolation method that generates images reflecting the characteristics of the missing ones by optimizing the latent variables of a Latent Diffusion Model based on the associated text information. We applied this method to generate substitute images for pretraining a VLM, specifically CLIP, and confirmed that the resulting zero-shot performance was comparable to or even better than that obtained using the original dataset before image loss. These results demonstrate that the proposed method can serve as a practical approach for supplementing datasets with missing images.

In recent years, there has been a growing demand to reduce the environmental impact of cutting workplaces. To solve this problem, a method has been devised to suppress the decay of cutting fluid and extend the service life of cutting fluid by usi ng alkaline cutting fluid, which is a mixture of cutting fluid and strong alkaline ionized water. The aim of this study is to investigate the effect of cutting oil concentration of alkaline cutting fluid on tool life for several workpiece materials such as SKD11, SUS304, and P20.The results showed that in the machining of SKD11 and SUS304, the tool life was shorter in alkaline cutting fluid compared to normal cutting fluid, and that in alkaline cutting fluid of the same pH, the higher the cutting fluid concentration, the longer the tool life tended to be. In P20 machining, tools lasted up to 114% (2.14 times) longer in alkaline cutting fluid than in normal cutting fluid, and the longer tool life was greater with higher cutting fluid concentra tion, but even oil-free cutting fluid (0% oil pH11) was found to have a 32% longer tool life effect.