This work presents a dynamic wireless power transfer system for an automated guided vehicle (AGV). Traditional AGV systems often rely on frequent battery replacement for power supply, which limits their range and significantly affects their operational efficiency. To address this issue, we propose using dynamic wireless power transfer technology to charge AGVs. This technology utilizes electromagnetic coupling between a transmitter and receiver to transfer energy. To achieve dynamic wireless power transfer, we have designed a comprehensive system architecture and validated its feasibility and effectiveness through experiments. The results demonstrate that the system can consistently supply the required energy to AGVs and achieve dynamic energy transfer during operation.
Category: ypec-2024
The project introduces a novel Auditory Orientation Training System (AOTS) named SoundScene, designed to enhance spatial awareness and independent navigation for visually impaired individuals. This system, leveraging AI technologies and mobile platforms, consists of two interconnected applications—one for trainees and one for trainers—operating over a local WiFi network using peer-to-peer connectivity. The trainee application uses spatial audio and augmented reality (AR) to create a 3D virtual environment, enabling users to orient themselves using auditory cues. This is achieved through the device’s camera and motion sensors, which adjust the spatial audio output to simulate real-world auditory scenarios. The trainer application facilitates real-time monitoring and dynamic scenario adjustment, enhancing the training experience. SoundScene can function in both connected and disconnected modes, ensuring versatile use in various settings. Evaluated through qualitative and quantitative measures, the system demonstrated significant improvements in the orientation and mobility skills of visually impaired users. It offers a scalable, cost-effective solution that harnesses widely available mobile technology, opening new avenues for training and assistance for the visually impaired, while also benefiting professions requiring enhanced auditory orientation.
The integration of generative artificial intelligence (Gen AI) holds significant promise for future engineers, particularly in mastering and applying complex technologies. This paper presents nanoCraft, a 3D modelling application developed by students to aid their peers in Information and Communication Technology (ICT). nanoCraft serves as an example to address the challenges beginners face in 3D modelling, such as navigating technical intricacies and steep learning curves. The development team, initially without 3D modelling knowledge, undertook an extensive learning process covering essential concepts like defining vertex data structures and UV mapping. This app encapsulates the team’s educational journey, offering a user-friendly platform for creating low-poly models and custom textures. nanoCraft is tailored for ICT students who prefer hands-on learning. It supports the full creative workflow, from initial model creation to augmented reality (AR) visualisation. By using nanoCraft, students not only learn theoretical aspects of 3D design but also gain practical experience through active creation and experimentation. This experiential learning approach, facilitated by nanoCraft, demonstrates the powerful combination of learning and implementation enabled by Gen AI, paving the way for innovative advancements in engineering education.
Amidst Hong Kong’s intense summer heat, 2023 witnessed fatal heatstroke cases, highlighting the urgent need for effective occupational heat protection. The government’s “Heat Stress at Work Warning” system, derived from the Summer Heat Index, has been critiqued for its inadequate reflection of actual working conditions, constrained by a limited weather station network. This paper examines these limitations and underscores the necessity for a robust system to enhance city resilience against escalating temperatures from global warming and urban heat island effects. We developed an innovative heatstroke prevention strategy that leverages AI and IoT for precise, localised summer heat index forecasts. A proof-of-concept prototype has been developed, illustrating the feasibility of a sophisticated warning system. This approach aims to deliver accurate, real-time alerts, thereby offering a tailored protection for the workforce against the growing threat of heat-related illnesses in the face of ongoing climate changes.
The project, titled Aico – an AI Collaborative Platform, aims to revolutionise AI interaction by developing a system where multiple AI bots can engage in meaningful and coherent dialogue with each other. Leveraging advanced natural language processing (NLP) techniques and machine learning algorithms, the project focuses on creating a robust conversational AI framework. This framework involves training a sophisticated language model using a vast corpus of conversational data to generate contextually appropriate responses. Additionally, a dialogue management system incorporating reinforcement learning and attention mechanisms will be developed to maintain coherence and context throughout interactions. Aico addresses the 1-on-1 human-AI conversation by enabling automated multi-modal AI collaboration among, thereby maximising their collective potential. The platform features a user-friendly Flow-Based Programming (FBP) interface, allowing users to visually program AI interactions and customise AI attributes, making it accessible even to non-technical users. The project is currently in its initial phase, concentrating on data collection, model development, and preliminary testing. The outcomes include enhanced AI conversational abilities, optimised dialogue management, and extensive evaluations to ensure performance. This project has the potential to significantly impact domains such as customer service, virtual assistants, and AI research, pushing the boundaries of AI conversation and interaction.
Imagine you are in a heated debate with a friend, and tension seems to escalate until you two share common ground. A relationship you have been building with a friend, partner, or someone you are close to, and everything diminish at just a flick of a switch, and you are left with tears and repent. In relationships, minute details matter, hence the choice of words and your attitude are paramount. We would like to introduce Gentlelens, smart AR glasses powered by AI technology that capture body gestures and facial expressions to analyse people’s sentiments. Besides, they are capable of capturing audio to analyse prosody to determine sentiment; after further analysis, our AI will provide guidance to excel in every conversation and to retire social barriers. All in all, the data we will receive or share can be used to train a more humane AI to develop social skills with users on a more appropriate occasion. There is not always a second chance in relationships. So we are here to save the first. Join us as we give everyone a chance to thrive in every conversation so they can turn challenges into opportunities.
Our research introduces a novel dynamic wireless charging system for Automated Guided Vehicles (AGVs), a rapidly growing market segment in the era of automation. The primary innovation of our project lies in enhancing AGV’s operational efficiency through dynamic wireless charging, offering significant market value. We employ a Tetris-shaped transmitting coil array, a unique approach that effectively mitigates primary-secondary mutual inductance fluctuation. Moreover, our system utilizes a newly proposed periodic energy control on the secondary side, eliminating the need for primary-secondary communication. These innovations not only improve AGV performance but also promise to revolutionize the wireless charging industry.
Due to the proliferation of information, both Artificial Intelligence (Al) and High-Performance Computing (HPC) are experiencing heightened demands. However, the use of conventional electronic integrated circuits, which consumes substantial physical space and energy during the transmission and processing of massive data, imposes limitations on the growth of HPC and Al. Consequently, the industry urgently needs a novel generation of high-speed and low-latency optical interconnection and computing technology. Leveraging the industry’s top-rated optical material, lithium niobate (LN), we have pioneered a wafer-scale nanofabrication technology. This innovation allows us to deliver a photonic system-on-chip that simultaneously features ultrahigh-speed electro-optic modulation and low-loss chip-scale integration while dramatically reducing size and electric energy consumption by >1000. A significant boost in device performance. We’re not just improving the technology – we’re redefining it.
MTR, a leading public transportation provider, has developed the “i-Centralized Platform (iCP)” to enhance the resilience and efficiency of its railway operations. The iCP is a cloud- based platform powered by AI and big data analytics, enabling data-driven decision-making and optimization of railway operations and maintenance. The iCP integrates data from a wide range of station facilities and infrastructure, leveraging an extensive LoRaWAN data network. This platform provides real-time visibility, predictive intelligence, and data-driven decision support, allowing MTR to move beyond reactive, manual management of the railway system. It empowers maintainers with real- time diagnostics and enables control center staff to optimize resource allocation based on unified data. Serving as a centralized hub for cross-functional data sharing and analysis, the iCP platform enables stakeholders to collectively review performance, identify issues, and develop solutions. This facilitates seamless coordination and a more proactive response to operational challenges. Additionally, the integration of sensors provides valuable real-time data for monitoring and decision-making, particularly on station E&M facilities. This data intelligence framework serves as a model for enhancing the resilience and efficiency of railway operation worldwide.
Regarding 132kV area transmission network, CLP typically have three configurations (Direct Feed, RMU Radial Feed, and RMU Open Ring). Each configuration obtains its unique characteristics and is deployed to specified scenario. This project aims to fully utilize the existing equipment in RMU Open Ring configuration and further enhance the operational flexibility in power grid. Simply said, this project proposes eliminating the normally open point in RMU Open Ring configuration that two bulk substations will be operated in parallel through plain feeders and RMUs circuits existing. The advantage of such operation is to provide higher flexibility for outage planning and contingency. In outage planning, N-1-1 criterion must be considered before switching off the circuit. Parallel operation aids for increase the maximum allowable substation loading for circuit outage. In contingency, RMU Close Ring Operation provides backup channels for electricity delivery. Such brings significant benefits to the power grid operation. Before implementing RMU Close Ring Operation, several considerations (Maximum Fault Level, Load Flow, and Protection Scheme) must be addressed. Under the use of existing equipment, permissive overreaching transfer tripping scheme is introduced. Although the proposal is technically feasible, the actual implementation subjects to the balance of system security, power quality, and operation flexibility.