Recently, convolutional neural network (CNN) compression via low-rank decomposition has achieved remarkable performance. Finding the optimal rank is a crucial problem because rank is the only hyperparameter for controlling computational complexity and accuracy in compressed CNNs. In this paper, we propose a global optimal rank selection method based on Bayesian optimization (BayesOpt), which is a machine learning based global optimization technique. By utilizing both a simple objective function and a proper optimization scheme, the proposed method produces a global optimal rank that provides a good trade-off between computational complexity and accuracy degradation. In addition, our method also reflects the correlation of each rank in multi-rank selection, and is able to flexibly yield an optimal rank with a given fixed compression ratio. Experimental results indicate that the proposed algorithm can identify the global optimal rank regardless of the huge size of dataset or the various structural features of CNNs. In all experiments on multi-rank selection, the proposed method produces the rank with higher accuracy and lower computational complexity than the state-of-the-art rank selection method, variational Bayesian matrix factorization (VBMF).

Monitoring and predicting the environment in an indoor space plays an important role in securing big data and detecting abnormal conditions in the industrial environment and living space. This study proposes an indoor multi-environment sensor system based on intelligent edge computing that collects and predicts environmental data. The system collects data using 14 types of environmental sensors and object detection technology models and implements a model that predicts indoor air quality based on the bi-directional LSTM network. The trained model shows high performance in predicting indoor air quality (IAQ) factors, such as CO${}_2$, PM2.5, and total volatile organic compounds (TVOC). The indoor multi-environment sensor system based on intelligent edge computing is available for data collection and environmental prediction in various spaces without restrictions on specific locations. This study proposes an integrated approach with various functions by applying edge computing to indoor environment monitoring. We verify the proposed system through various experiments.

Filter pruning is necessary to efficiently deploy convolutional neural networks on edge devices that have limited computational resources and power budgets. With conventional filter pruning techniques, the same pruning rate is manually specified for different convolutional layers, which is suboptimal and time-consuming. To extract the features from the coarse level to the fine level, the number of filters in each layer has various distributions. Therefore, it is unsuitable to utilize the same pruning rate for different functional layers. To address this issue, we propose a high-dimensional Bayesian optimization-based filter pruning (HDBOFP) algorithm, which aims to automatically determine the most appropriate pruning rate for each convolutional layer. In addition, the proposed method can automatically identify optimal pruning-rate combinations without a time-consuming retraining phase. Compared with conventional filter pruning methods, this automated filter pruning technique exhibits a higher efficiency, which improves accuracy and reduces the required human labor. The effectiveness of our automated filter pruning algorithm is validated through two major computer vision applications, namely image classification and object detection. Specifically, when used in combination with ResNet-110 to classify the CIFAR-10 dataset, HDBOFP reduces the required number of float point operations (FLOPs) by more than 62% without affecting accuracy. Similarly, when HDBOFP is added to the YOLOv5l framework to run detection experiments on the MS-COCO 2017 dataset, FLOPs decrease by more than 43% with only a 1.2% loss in mean average precision, which has advanced the previous studies.

To understand high-order intrinsic key patterns in high-dimensional data, tensor decomposition is a more versatile tool for data analysis than standard flat-view matrix models. Several existing tensor models aim to achieve rapid computation of high-order principal components based on the tensor power method. However, since a tensor power method does not enforce orthogonality in subsequently calculated decomposition components, it causes far more challenges on principal component analysis of high-order tensors. To address this problem, several tensor power method variant algorithms incorporating sparsity into decomposition factors have been proposed. However, because these variant algorithms require additional procedures based on data-driven hyper-parameter optimization algorithms, a trade-off between computational cost and convergence exists. In this paper, a novel tensor power method called the fast circulant tensor power method is proposed. The proposed algorithm combines tensor-train decomposition and the power method. Tensor-train decomposition is a high-order tensor decomposition method based on auxiliary unfolding matrix decomposition. Thus, the power method can be embedded into our methodology without any additional processes. Notably, a simple combination of these two methods may cause a local optima problem because the power method only guarantees convergence on each unfolding matrix in tensor-train decomposition. To solve this problem, the circulant updating method is proposed, which globally optimizes all factor vectors by reordering some steps of the factor vector updates. It is experimentally demonstrated that, compared to state-of-the-art tensor power method variant methodologies, the proposed algorithm achieves the lowest computational complexity and quantitatively good performance in various applications including large-scale color image decomposition and convolutional neural network compression.

Traffic sign recognition is a pivotal technology in the advancement of autonomous vehicles as it is critical for adhering to country- or region-specific traffic regulations. Defined as an image classification problem in computer vision, traffic sign recognition is a technique that determines the class of a given traffic sign from input data processed by a neural network. Although image classification has been considered a relatively manageable task with the advent of neural networks, traffic sign classification presents its own unique set of challenges due to the similar visual features inherent in traffic signs. This can make designing a softmax-based classifier problematic. To address this challenge, this paper presents a novel traffic sign recognition model that employs angular margin loss. This model optimizes the necessary hyperparameters for the angular margin loss via Bayesian optimization, thereby maximizing the effectiveness of the loss and achieving a high level of classification performance. This paper showcases the impressive performance of the proposed method through experimental results on benchmark datasets for traffic sign classification.

The generation of 3D digital humans has traditionally relied on multi-view imaging systems and large-scale datasets, posing challenges in cost, accessibility, and real-time applicability. To overcome these limitations, this study presents an efficient pipeline that constructs high-fidelity 3D digital humans from a single frontal image. By leveraging generative AI, the system synthesizes additional views and generates UV maps compatible with the SMPL-X model, ensuring anatomically accurate and photorealistic reconstructions. The generated 3D models are imported into Unity 3D, where they are rigged for real-time motion synchronization using BlazePose-based lightweight pose estimation. To further enhance motion realism, custom algorithms—including ground detection and rotation smoothing—are applied, improving movement stability and fluidity. The system was rigorously evaluated through both quantitative and qualitative analyses. Results show an average generation time of 211.1 s, segmentation accuracy of 92.1%, and real-time rendering at 64.4 FPS. In qualitative assessments, expert reviewers rated the system using the SUS usability framework and heuristic evaluation, confirming its usability and effectiveness. This method eliminates the need for multi-view cameras or depth sensors, significantly reducing the barrier to entry for real-time 3D avatar creation and interactive AI-driven applications. It has broad applications in virtual reality (VR), gaming, digital content creation, AI-driven simulation, digital twins, and telepresence systems. By introducing a scalable and accessible 3D modeling pipeline, this research lays the groundwork for future advancements in immersive and interactive environments.