Abstract

Direct Load Control(DLC) program is an advanced agreement between a power utility and customers to control some customers’ appliances (e.g., air conditioners and water heaters). It is an incentive-based demand response system in which the utility provides the affected customer with financial incentives. Under the existing DLC program, the utility turn on/off customers’ appliances without considering the customer's inconvenience. that causes a lot of customers are reluctant to participate in the DLC program. In this paper, To solve this problem, the author present weather based intelligent demand controller to analyze correlation between weather condition and customers energy consumption and to predict customers energy consumption. To demonstrate the validity of the proposed demand controller, porto type demand controller are developed and applied to real customer load.

Abstract

In the VPN (with data encryption) applied to protect the distributed network over the public internet, the small computing resource of low-spec devices limits TCP/IP-based VPN use. Although lightweight IoT communication protocols such as LoWPAN are used, TCP/IP-based VPNs such as IPsec and OpenVPN require bandwidth, CPU/memory, and electric power.

To provide a safe and lightweight network, we propose a secure and scalable IoT platform (SSI) that can prevent security threats while minimizing the usage of computing resources. SSI, which has a lower load than TCP/IP-based VPN, is a layer 2 VPN and supplies data link frame encryption. L2TP and VXLAN are provided for a scalable layer 2 VPN, and the MACsec algorithm encrypts layer 2 frames. Furthermore, SSI shows 30% network speed improvement and 31.6% CPU usage reduction compared to the network applied OpenVPN.

Abstract

Modern challenges—such as the growing volume of freight and passenger transportation, the need to reduce greenhouse gas emissions, and the global shift toward sustainable technologies—demand a rethinking of energy strategies in Kazakhstan's railway sector. This keynote will explore key development directions including electrification, the integration of renewable energy sources, improvements in rolling stock energy efficiency, and the potential use of alternative fuels. Special attention will be given to the role of rail transport energy in achieving the national decarbonization strategy and building a sustainable transportation infrastructure.

Abstract

Adversarial modifications of input data can degrade the stability of deep neural networks in medical image segmentation. This study evaluates the robustness of UNet and Att-UNet++ architectures using the NuInsSeg dataset with annotated nuclear regions from various tissue sources. Both models were trained and tested under eight perturbation types, including gradient-based, iterative, and stochastic methods, with identical parameter settings. In the absence of perturbations, Att-UNet++ produced higher segmentation results with a Dice of 0.7160 and a mean IoU of 0.6190 compared to 0.6424 and 0.4732 for UNet. Under NI-FGSM and Gaussian noise, Att-UNet++ experienced a greater reduction in mean IoU, reaching 0.1215 and 0.0658, while UNet maintained 0.1968 and 0.2329. Loss landscape analysis showed smoother surfaces for Att-UNet++, yet revealed increased responsiveness to directional gradients. The findings suggest that improvements in segmentation accuracy through architectural modifications may be accompanied by increased vulnerability to input perturbations, highlighting the necessity of robustness evaluation in model development for medical image analysis.

Abstract

The increasing demand for sustainable and reliable railway transportation has spurred innovations in traction power supply systems, particularly through the integration of microgrids. This talk presents a comprehensive overview of resilient traction power supply systems in modern railways, emphasizing the role of interconnected microgrids in enhancing operational reliability, energy efficiency, and system flexibility.

We explore key architectural advancements that enable bi-directional power flow, dynamic load management, and fault tolerance. Particular attention is given to the coordination between railway substations, renewable energy sources, energy storage systems, and intelligent control frameworks that form the backbone of interconnected microgrids. Case studies and simulation insights highlight how these systems can effectively mitigate power outages, reduce dependency on centralized grids, and support the transition toward greener railway networks. The speech concludes with a discussion on current challenges, future research directions, and policy implications for large-scale deployment.

Abstract

The presentation begins with a comprehensive overview of Vietnam’s power system, encompassing the structure and current status of electricity generation, high-voltage transmission networks, and the distribution and retail sector. The next section will discuss key development directions for Vietnam’s power system based on the Power Development Plan 8. Following this, the presentation addresses some pressing research issues for Vietnam’s power system. In the final part, the presentation highlights Vietnam’s latest policies to promote innovation in the energy sector, including new regulations, R&D incentives, and increased collaboration among industry, academia, and policymakers. These efforts aim to foster technological advancement and support the sustainable growth of Vietnam’s power sector.

Abstract

The global transition toward renewable energy and intelligent computing is driving the development of multifunctional, environmentally integrated technologies. This study presents innovations in transparent photovoltaics and neuromorphic devices designed for both terrestrial and underwater applications. A highly efficient transparent photovoltaic (TPV) module was developed, comprising an FTO/n-ZnO/p-NiO/silver nanowires (AgNWs)/ZnO structure with a transparent p–n junction. By employing metal sputtering techniques to assemble module arrays, we significantly increased the voltage output per unit area, offering new opportunities for building-integrated transparent photovoltaics (BITPVs) that generate solar energy without obstructing visibility.

In parallel, we introduce a water-enhanced transparent photovoltaic system for underwater power generation, addressing critical needs in sensing, networking, and communication for ocean exploration. This wide-bandgap heterojunction device achieves an average visible transmittance of 41% and an underwater power conversion efficiency of 10.25% under monochromatic 470 nm light. The water medium not only focuses incident light but also improves light collection angles, while impedance spectroscopy reveals that a water-induced electric double layer enhances electron transport and photogeneration. Demonstrating its practicality, the system successfully powered a 10 mW load underwater, establishing a pathway toward secure, energy-autonomous underwater environments.

Additionally, we report on a photonic neuromorphic device based on 2D layer (SnS) into the transparent heterojunction device, capable of performing basic logic operations (OR, NOT, AND) using light as a stimulus. This artificial synapse emulates neurotransmitter-like behavior, modulating signal processing for precise control of logic functions. Such a system promises significant advancements in neuromorphic computing, with potential applications in AI, humanoid robotics, and adaptive intelligent electronics.

Together, these developments represent a significant step toward seamlessly integrated energy-harvesting, intelligent computing, and environmentally adaptive systems for the future.