Dynamic performance of control loops and their interactions in a VAV HVAC system

Good HVAC control schemes in buildings help reduce energy use and maintain occupant comfort. During the last 10 years, VAV-HVAC systems are widely used in commercial buildings because they can reduce energy use significantly and provide good temperature control of the conditioned spaces compared to CV-HVAC systems. However, the controls for VAV systems are somewhat difficult and further research in this area still needs to be carried out. This research will focus on local control loop interactions and operating strategies for VAV control systems. To reach this objective, experiments were conducted in a two-zone VAV test facility. The facility consists of six control loops (cooling valve, fan speed, two dampers, two electric heaters) activated by DDC PI controllers. Experiments were conducted in this facility and the results were processed, and analyzed in this thesis. The steady state and dynamic characteristics of a VAV-HVAC control system has been studied. The interaction between control loops of a VAV system operating under (i) open-loop, (ii) closed-loop control modes were evaluated. Also six local loops were tuned and optimal range of controller gains for single-loop and multi-loop VAV system operation were determined. Experiments were conducted to compare the operating performance of Pressure Independent Control (PIC) and Pressure Dependent Control (PDC) strategies. The results show that PIC strategy is more stable than PDC. However, a finely tuned PDC strategy gives good room temperature control compared to PIC. The results of this research will help building engineers identify operating problems and interactions between local loops in VAV systems. In addition, the optimal range of controller gains obtained in this research can offer guidelines in PID controller tuning. Moreover, the tests conducted provide experimental data to building engineers and researchers for model development and control desig

Instance Needs More Care: Rewriting Prompts for Instances Yields Better Zero-Shot Performance

Enabling large language models (LLMs) to perform tasks in zero-shot has been an appealing goal owing to its labor-saving (i.e., requiring no task-specific annotations); as such, zero-shot prompting approaches also enjoy better task generalizability. To improve LLMs' zero-shot performance, prior work has focused on devising more effective task instructions (e.g., ``let's think step by step'' ). However, we argue that, in order for an LLM to solve them correctly in zero-shot, individual test instances need more carefully designed and customized instructions. To this end, we propose PRoMPTd, an approach that rewrites the task prompt for each individual test input to be more specific, unambiguous, and complete, so as to provide better guidance to the task LLM. We evaluated PRoMPTd on eight datasets covering tasks including arithmetics, logical reasoning, and code generation, using GPT-4 as the task LLM. Notably, PRoMPTd achieves an absolute improvement of around 10% on the complex MATH dataset and 5% on the code generation task on HumanEval, outperforming conventional zero-shot methods. In addition, we also showed that the rewritten prompt can provide better interpretability of how the LLM resolves each test instance, which can potentially be leveraged as a defense mechanism against adversarial prompting. The source code and dataset can be obtained from https://github.com/salokr/PRoMPTdComment: Work in progres

Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models

In media industry, the demand of SDR-to-HDRTV up-conversion arises when users possess HDR-WCG (high dynamic range-wide color gamut) TVs while most off-the-shelf footage is still in SDR (standard dynamic range). The research community has started tackling this low-level vision task by learning-based approaches. When applied to real SDR, yet, current methods tend to produce dim and desaturated result, making nearly no improvement on viewing experience. Different from other network-oriented methods, we attribute such deficiency to training set (HDR-SDR pair). Consequently, we propose new HDRTV dataset (dubbed HDRTV4K) and new HDR-to-SDR degradation models. Then, it's used to train a luminance-segmented network (LSN) consisting of a global mapping trunk, and two Transformer branches on bright and dark luminance range. We also update assessment criteria by tailored metrics and subjective experiment. Finally, ablation studies are conducted to prove the effectiveness. Our work is available at: https://github.com/AndreGuo/HDRTVDM.Comment: Accepted by CVPR202

A Local Signal based Inter-area Damping Controller via Dynamic State Estimation Approach

To suppress inter-area oscillations and enhance small-signal stability of power systems, wide-area damping controllers (WADC) have been used by utilising wide-area signals with high observabilities to inter-area modes. However, the requirement of the wide-area signal makes communication systems involved in the control loops of the power systems and therefore, the damping performance of the conventional WADC suffers from time-delay, data dropout and cyber-attacks. This paper proposes a local signal based inter-area damping controller (LSIADC) to suppress inter-area oscillation without using wide-area signals. The LSIADC extracts a signal with high observability to the inter-area mode from the local signal by dynamic state estimation (DSE) technique and the control signal is obtained by adding a proper phase shift to the extracted signal. The simulation results show that the proposed controller can effectively suppress inter-area oscillation using the local signal only

Diving into Darkness: A Dual-Modulated Framework for High-Fidelity Super-Resolution in Ultra-Dark Environments

Super-resolution tasks oriented to images captured in ultra-dark environments is a practical yet challenging problem that has received little attention. Due to uneven illumination and low signal-to-noise ratio in dark environments, a multitude of problems such as lack of detail and color distortion may be magnified in the super-resolution process compared to normal-lighting environments. Consequently, conventional low-light enhancement or super-resolution methods, whether applied individually or in a cascaded manner for such problem, often encounter limitations in recovering luminance, color fidelity, and intricate details. To conquer these issues, this paper proposes a specialized dual-modulated learning framework that, for the first time, attempts to deeply dissect the nature of the low-light super-resolution task. Leveraging natural image color characteristics, we introduce a self-regularized luminance constraint as a prior for addressing uneven lighting. Expanding on this, we develop Illuminance-Semantic Dual Modulation (ISDM) components to enhance feature-level preservation of illumination and color details. Besides, instead of deploying naive up-sampling strategies, we design the Resolution-Sensitive Merging Up-sampler (RSMU) module that brings together different sampling modalities as substrates, effectively mitigating the presence of artifacts and halos. Comprehensive experiments showcases the applicability and generalizability of our approach to diverse and challenging ultra-low-light conditions, outperforming state-of-the-art methods with a notable improvement (i.e., $\uparrow$5\% in PSNR, and $\uparrow$43\% in LPIPS). Especially noteworthy is the 19-fold increase in the RMSE score, underscoring our method's exceptional generalization across different darkness levels. The code will be available online upon publication of the paper.Comment: 9 page