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This study introduces "shortcut routing," a novel routing mechanism in capsule networks that addresses computational inefficiencies by directly activating global capsules from local capsules, eliminating intermediate layers. An attention-based approach with fuzzy coefficients is also explored for improved efficiency. Experimental results on Mnist, smallnorb, and affNist datasets show comparable classification performance, achieving accuracies of 99.52%, 93.91%, and 89.02% respectively. The proposed fuzzy-based and attention-based routing methods significantly reduce the number of calculations by 1.42 and 2.5 times compared to EM routing, highlighting their computational advantages in capsule networks. These findings contribute to the advancement of efficient and accurate hierarchical pattern representation models., 8 pages, published at IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E104.A(8)

Utilizing pre-trained models involves fully or partially using pre-trained parameters as initialization. In general, configuring a pre-trained model demands practitioners’ knowledge about problems or an exhaustive trial–error experiment according to a given task. In this paper, we propose tuning trainable layers using a genetic algorithm on a pre-trained model that is fine-tuned on single-channel image datasets for a classification task. The single-channel dataset comprises images from grayscale and preprocessed audio signals transformed into a log-Mel spectrogram. Four deep-learning models used in the experimental evaluation employed the pre-trained model with the ImageNet dataset. The proposed genetic algorithm was applied to find the highest fitness for every generation to determine the selective layer tuning of the pre-trained models. Compared to the conventional fine-tuning method and random layer search, our proposed selective layer search with a genetic algorithm achieves higher accuracy, on average, by 9.7% and 1.88% (MNIST-Fashion), 1.31% and 1.14% (UrbanSound8k), and 2.2% and 0.29% (HospitalAlarmSound), respectively. In addition, our searching method can naturally be applied to various datasets of the same task without prior knowledge about the dataset of interest.

열화상 카메라는 제한된 열화상 해상도로 인해 잡음이 있는 영상을 야기한다. 본 논문에서는 잡음 문제를 해결하기 위해 반복 가능한 인셉션-레지듀얼 블록(IRB)으로 이루어진 새로운 딥러닝 기반의 신경망을 제안한다. 각각의 IRB는 원본 이미지에 대하여 서로 다른 수용 영역을 가진 합성곱 층 2개를 가지고 베니싱 그레디언트(vanishing gradient)를 방지하기 위한 하나의 쇼트 컷(shortcut connection)으로 구성된다. 제안된 방법은 12개의 열화상 이미지로 테스트가 이루어졌다. 실험 결과, 제안된 방법은 최신의 잡음 제거 방법인 DnCNN과 비교해 봤을 때 신호대잡음비(PSNR)를 39.57에서 40.26으로 처리속도는 1.5910초에서 0.7508초로 잡음 제거 성능 및 처리 속도 개선을 보여준다.

Forecasting, commonly used in econometrics, meteorology, or energy consumption prediction, is the field of study that deals with time series data to predict future trends. Former studies have revealed that both traditional statistical models and recent deep learning-based approaches have achieved good performance in forecasting. In particular, temporal convolutional networks (TCNs) have proved their effectiveness in several time series benchmarks. However, presented TCN models are too heavy to deploy on resource-constrained systems, such as edge devices. As a resolution, this study proposes a stride–dilation mechanism for TCN that favors a lightweight model yet still achieves on-pair accuracy with the heavy counterparts. We also present the Chonnam National University (CNU) Electric Power Consumption dataset, the dataset of energy consumption measured at CNU by smart meters every hour. The experimental results indicate that our best model reduces the mean squared error by 32.7%, whereas the model size is only 1.6% compared to the baseline TCN.

Over a decade, convolutional neural networks (CNNs) have been applied extensively on various tasks related to images. Given an input image, a CNN model will investigate the content and deduce the representation of this image using a model's structure built from hidden neurons. This representation analyzes data semantically, which helps to solve semantic issues, such as image retrieval. To verify the above viewpoint, this study addresses the problem of using features learned from a CNN model to perform image retrieval. To more emphasize the efficiency of learned features, we consider degraded images and their enhanced version as queries and search for similar ones in the gallery set. Data augmentation is also applied to increase the number of images in the gallery. The experiments are conducted on a multi-view dataset, smallNorb. Experimental results are reported both in quantity and quality.

In this paper, the defective product classification based on deep learning for a smart factory is introduced. The proposed system contains PLC (Programmable Logic Controller), Artificial Intelligence (AI) embedded board and cloud service. The AI embedded board is connected and communicated to receive and send commands to PLC via SPI (Serial Peripheral Interface) protocol. The pre-trained defective product classification model is uploaded, saved on a cloud server and downloaded to AI Embedded board for each particular product. The core technique of the system is the AI-based embedded board. Due to the limitation of label data, we use transfer learning method to retrain deep neural networks (DNN). We implement and compare the classification results on different deep neural network including ResNet, DenseNet, and GoogLeNet. We trained these networks by GPU server on casting product classification data. After that, the pre-trained models are optimized and applied on practical embedded board. The experimental results show that our system is able to classify defective products with high accuracy and fast speed.

Researchers and entertainment companies have given lots of attention to virtual reality over the past decade. 3D multi-view is a technology that provides interactions that are similar to those in the real world. However, 3D video streaming has a high data transfer rate because we must transmit multimedia data at a rate several times higher than that used for regular streaming. Besides, network throughput is unstable due to the inherent limitations of network infrastructure, which degrades video streaming quality. Additionally, network failure can occur frequently, causing stalling in multimedia playback. Hence, a network system is required to have more than one backup route in order to successfully guarantee the reliability of a network at all times. Furthermore, in the field of multi-view transmission, not much research has been published that has been conducted in a network virtualization environment. Therefore, we present a study on adaptive-based, high-efficiency video coding with three-dimensional, multi-view streaming over a peer-to-peer network. First, we study adaptive bitrate streaming methods based on high-efficiency video coding. Then we research transmitting multi-view data over a multi-path system. In the experiment, we first record a video from different views using five cameras. Next we merge recorded videos from the five cameras into a file and encode it before transmitting it over the peer-to-peer network. Moreover, we build a virtualized system using Docker virtualization technology and network function virtualization. The results of the experiment show that transmitting high-volume data over a multi-path network channel increases the streaming buffer level, which is about 20% higher than an adaptive streaming 3D method. It also makes the video quality 4% higher than in an HEVC-based adaptive streaming method.