The proliferation of immersive 3D web applications, from e-commerce product viewers to virtual real estate tours, has created a critical need for high-quality, real-time rendering directly within the browser. Neural radiance fields (NeRF) offer unprecedented photorealism but are hamstrung by immense computational demands, making their deployment on resource-constrained web platforms a significant web engineering challenge. The core bottleneck is NeRF’s reliance on dense point sampling for volume rendering. This paper introduces a novel framework that directly tackles this challenge through a pioneering adaptive sampling technique powered by reinforcement learning. We name this framework PPO-NeRF. It integrates the rapid training capabilities of Instant-NGP’s hash encoding with an agent trained via proximal policy optimization (PPO). This agent learns to adaptively predict the minimal set of crucial sample points along each camera ray, dynamically pruning computationally redundant samples to optimize rendering specifically for web-based, real-time scenarios. Experimental results demonstrate that PPO-NeRF significantly lowers the barrier to web deployment. Compared to the original NeRF, it reduces training time by approximately 73.63%, enabling faster content iteration for web developers. More critically, our adaptive sampling slashes rendering time by approximately 44.7% and VRAM usage by approximately 29.9%, while maintaining comparable visual fidelity. These gains directly translate to faster load times, smoother user interaction, and broader device compatibility. In conclusion, PPO-NeRF provides a practical solution to NeRF’s long-standing performance bottlenecks, establishing a viable pathway for deploying high-fidelity, interactive 3D experiences at scale across the modern web.

As modern smart-factory environments increasingly require real-time remote operation and lightweight cloud-based control, routing intelligence for OHT systems must be fully web-app compatible, supporting scalable deployment without reliance on high-end local infrastructure. To address these demands and the limitations of static algorithms in large-scale OHT systems, this study proposes a multi-agent reinforcement learning model based on proximal policy optimization, incorporating a state space that accounts for chain blockage probability. The key metric, “movement success probability,” integrates preceding agent states to predictively assess chain-reaction congestion, enabling agents to proactively select stable detours. To enhance scalability in high-density environments, the model stabilizes learning through a lightweight policy initialization approach rather than requiring large-scale training from scratch. Moreover, the proposed decentralized structure minimizes central computational overhead, aligning naturally with web-app deployment and enabling real-time monitoring across distributed environments. In a simulation with 1333 nodes and 100 OHTs, the proposed model achieved an average task completion distance of 166,809 mm, improving efficiency by 4.1% over the rule-based Floyd–Warshall method (173,940 mm). Notably, in worst-case scenarios where the rule-based method surged to 321,753 mm due to congestion, the AI model maintained 176,268 mm, achieving a 45.2% reduction and demonstrating superior operational stability.

Latent diffusion models (LDMs) have rapidly become the de facto backbone of web-scale generative systems, powering text-to-image platforms such as Stable Diffusion and their video, 3D, and domain-specific extensions. By performing the diffusion process in a compressed latent space rather than directly in pixel space, LDMs achieve a favorable trade-off between computational efficiency and generative fidelity, enabling deployment in interactive web applications and large-scale content pipelines. This paper presents a comprehensive survey of LDMs from the perspective of both foundational modeling and web engineering. We first review the background of diffusion models and latent representations, contrasting LDMs with classical VAEs, GANs, and pixel-space diffusion models. We then dissect the architectural design of LDMs, including autoencoder backbones, latent-space U-Nets and diffusion transformers, conditioning mechanisms, training objectives, and sampling accelerations. Building on recent general surveys of diffusion models in vision, temporal data, and inverse problems, we propose a taxonomy of LDM variants, covering 2D image models, video and 4D models, and domain-specific LDMs in medical imaging, watermarking, time series, and text. From a web engineering viewpoint, we analyze LDM-based services exposed via web APIs, hosted user interfaces, and developer platforms, and discuss system-level concerns such as scalability, latency, cost, safety, and governance. We review current evaluation methodologies (quality, diversity, downstream task performance, robustness, watermarking) and highlight open challenges in controllability, interpretability, resource efficiency, and regulatory compliance, especially in light of recent legal and societal developments around generative deepfakes and copyright. This survey aims to provide both a conceptual map of LDM research and practical guidance for designing, deploying, and governing LDM-driven web systems.

In recent years, 3D reconstruction and rendering technologies have become increasingly important in various web-based applications within the field of web technology. In particular, with the emergence of technologies such as WebGL and WebGPU, which enable real-time 3D content rendering in web browsers, immersive experiences and interactions on the web have been significantly enhanced. These technologies are widely used in applications such as 3D visualization of virtual products or 3D exploration of building interiors on real estate websites. Through these advancements, users can experience 3D content directly in their browsers without the need to install additional software, greatly expanding the possibilities of the web. Amidst this trend, the neural radiance field (NeRF) has garnered attention as a cutting-edge technology that improves the accuracy of 3D reconstruction and rendering. NeRF is a technique widely used in computer vision and graphics for reconstructing 3D spaces from 2D images taken from multiple viewpoints. By predicting the color and density of each pixel, NeRF captures the complex 3D structure and optical properties of a scene, enabling highly accurate 3D reconstructions. However, NeRF’s primary limitation is the time-consuming nature of both the training and inference processes. Research efforts to address this issue have focused on two key areas: optimizing network architectures and training procedures to accelerate scene learning, and improving inference speed for faster rendering. While progress has been made in enhancing training speed, challenges remain in improving the inference process. To address these limitations, we propose a two-step approach to significantly improve NeRF’s performance. First, we optimize the training phase through a multi-resolution hash encoding technique, reducing the computational complexity and speeding up the learning process. Second, we accelerate the inference phase by caching the input data of the NeRF MLP, which allows for faster rendering without sacrificing quality. Our experimental results demonstrate that this approach reduces training time by 68.42% and increases inference speed by 98.18%.

Detecting backgrounds in videos is an important technology that can be used for many applications such as management of major facilities and military surveillance depending on the purpose. It is difficult to accurately find and identify important objects in the background if there are obstacles such as pedestrian or car in the video. In order to overcome this problem, the following method is used to detect the background. First, a pixel area histogram is generated to determine the amount of change in pixel units of an image over time. Based on the histogram, we propose an algorithm that estimates the background by selecting the case with the smallest rate of change. In addition, in order to strongly respond to changes in the surrounding environment, even when a change in brightness occurs, this is solved through frame overlap. Finally, the desired object is identified by applying YOLO v3 as a model for object detection in the obtained background. Through the above process, this study proposes a method for effectively identifying static objects in the background by precisely estimated background of the video. Experimental results show that the non-detection and false detection rate for the background object is enhanced by 60.2% and 11.2%, respectively, in comparison with when the proposed method was not applied.

In recent years, attempts have been made to create a door-opening or elevator button that operates based on gestures when entering and exiting a building. This can consider the convenience of an individual carrying luggage, and in some cases, has the advantage of preventing the spread of disease between people through contact. In this study, we propose a method for operating elevator buttons without contact. Elevators cannot utilize high-performance processors owing to production costs. Therefore, this paper introduces a prototype of a low-performance processor-based system that can be used in elevators, and then introduces a weighted K-nearest neighbors (K-NN) based user gesture learning and number matching method for application in an optimal non-contact button control method that can be used in such an environment. As a result, through the proposed method, a performance gain of 7.5% in comparison to a conventional K-NN method and a performance improvement of 9.7% compared to a radial basis function were achieved in a relatively low-performance processor-based system.

The mobile GPU (Graphic Processing Unit) market has grown steadily due to expansion of the mobile game industry. Despite the rapid computation capability of mobile devices, handling a large amount of high-quality graphics in real-time is difficult. Therefore, effective technologies for improving mobile GPU in smartphones are required. In this thesis, we examine the trade-off between quality and performance, and address the benefits of graphic performance improvement by degrading quality. To implement this idea, we propose performance optimization methodologies for 3D applications using an OpenGL ES library hooking method. Our methodologies do not require any source code from 3D applications, and can be applied to any Android phones that use OpenGL ES in real-time. To demonstrate the benefits of our methodology, we conducted performance verifications of five well-known benchmarks using a smartphone, and measured the quality in accordance with each methodology. In addition, we showed the optimal trade-offs between quality and performance. By using the proposed technique, the performance of mobile GPU can be significantly improved to achieve a better trade-off between quality and performance.

For ubiquitous computing environments, an important parameter is whether all the components in the specific environment can connect with one another. Given this capability, we can share various kinds of content across mobile terminals. This paper introduces an effective scheme to manage multimedia sharing based on specially designed profiles and a virtual community. A virtual community is defined as any specific group of users connected for a common interest. Specifically the proposed scheme consists of two layers, i.e., a community construction layer and a multimedia sharing layer, based on personal spaces, which are responsible for constructing and managing the multimedia sharing community. The community construction layer, which is designed to be run on the mobile terminals, provides an effective way to find community members simultaneously, based on specially designed profiles, such as a user profile and an abstract profile. The multimedia sharing layer is responsible for sharing multimedia content, and is constructed as a specially designed scheme based on locality. The proposed scheme provides an effective multimedia sharing mechanism within a community. Simulation results show that the number of messages and the time required for community member discovery is reduced by 32% and 73%, respectively, in comparison with the conventional DHT-based scheme. The approach also reduces the time to exchange content by 50% with respect to the same baseline.