Loading Planar Fixtures in the Presence of Geometric Uncertainty

Fixtures are used in almost all phases of machining and assembly. The purpose of a fixture is to accurately position and hold a part. In this paper we consider the problem of loading a part into a fixture. Machinist's often refer to the "3-2-1" method: load the part into 3-point contact with a reference surface, slide it into 2-point contact with locators, and finally apply a 1-point clamping contact. Similarly, we propose to use compliant motion to achieve a precise final configuration. We consider a planar scenario, where a given polygonal part must be loaded into a given fixture consisting of 4 point contacts. Three of these contacts will be treated as locators. The remaining contact -- applied last -- serves as the clamp. A loading plan defines which contact will be treated as the clamp, a commanded velocity for the part, and a range of initial positions of the part that are guaranteed to reach the desired configuration under the commanded velocity. We develop a version of the back..

Realistic Image Generation from Text by Using BERT-Based Embedding

Recently, in the field of artificial intelligence, multimodal learning has received a lot of attention due to expectations for the enhancement of AI performance and potential applications. Text-to-image generation, which is one of the multimodal tasks, is a challenging topic in computer vision and natural language processing. The text-to-image generation model based on generative adversarial network (GAN) utilizes a text encoder pre-trained with image-text pairs. However, text encoders pre-trained with image-text pairs cannot obtain rich information about texts not seen during pre-training, thus it is hard to generate an image that semantically matches a given text description. In this paper, we propose a new text-to-image generation model using pre-trained BERT, which is widely used in the field of natural language processing. The pre-trained BERT is used as a text encoder by performing fine-tuning with a large amount of text, so that rich information about the text is obtained and thus suitable for the image generation task. Through experiments using a multimodal benchmark dataset, we show that the proposed method improves the performance over the baseline model both quantitatively and qualitatively

Realistic Image Generation from Text by Using BERT-Based Embedding

Recently, in the field of artificial intelligence, multimodal learning has received a lot of attention due to expectations for the enhancement of AI performance and potential applications. Text-to-image generation, which is one of the multimodal tasks, is a challenging topic in computer vision and natural language processing. The text-to-image generation model based on generative adversarial network (GAN) utilizes a text encoder pre-trained with image-text pairs. However, text encoders pre-trained with image-text pairs cannot obtain rich information about texts not seen during pre-training, thus it is hard to generate an image that semantically matches a given text description. In this paper, we propose a new text-to-image generation model using pre-trained BERT, which is widely used in the field of natural language processing. The pre-trained BERT is used as a text encoder by performing fine-tuning with a large amount of text, so that rich information about the text is obtained and thus suitable for the image generation task. Through experiments using a multimodal benchmark dataset, we show that the proposed method improves the performance over the baseline model both quantitatively and qualitatively