Search Results (622)

Objective: To explore the impact of supine retroperitoneal single-port robot-assisted partial nephrectomy with lower anterior access on perioperative ventilatory, cardiovascular, and pain-related outcomes compared to a cohort of patients treated with single-port robot-assisted retroperitoneal or transperitoneal partial nephrectomy with standard flank patient positioning. Materials and Methods: Clinical and surgical data of all consecutive patients treated with single-port robot-assisted partial nephrectomy between March 2019 and January 2024 were prospectively collected and retrospectively analyzed. Specific same-day-discharge guidelines were applied to all cases. Failed same-day discharge was defined as the presence of early (<90 days) perioperative complications or the absence of opioid-free postoperative recovery. Results: Overall, 105 consecutive patients treated with single-port robot-assisted partial nephrectomy were analyzed. No differences emerged in baseline features. Peak inspiratory pressure and plateau pressure changes were significantly lower in the supine retroperitoneal lower anterior access group from the time of CO₂ insufflation throughout every 30-min operative setpoint assessment (p = 0.02, p = 0.03, and p = 0.02, respectively). The transperitoneal group showed significantly higher values of mean, systolic, and diastolic blood pressure compared to retroperitoneal approaches. The supine lower anterior access group also showed significantly lower non-surgical operative room time, perioperative opioid administration, and postoperative median VAS pain score. Conclusions: The adoption of supine lower anterior access improved perioperative ventilatory, cardiovascular, and pain-related outcomes, also optimizing operating room efficiency. Further multicenter series with longer follow-ups are still needed to validate our preliminary results. Full article

This paper proposes a Distributed Cooperative Algorithm (DCA) that solves the windup problem caused by the saturation of the Distributed Energy Resource (DER) PI-based control unit. If the reference reactive current output by the PI exceeds the maximum reactive power capacity of the DER, the control unit saturates, preventing the optimal voltage regulation at the connection node of the Active Distribution Network (ADN). Instead of relying on a centralized solution, we proposed a cooperative approach in which each DER’s control unit takes part in the DCA. If a control unit saturates, the voltage regulation error is not null, and the algorithm is activated to assign a share of this error to all DERs’ control units according to a weighted average principle. Subsequently, the algorithm determines the control unit’s new value of the voltage setpoint, desaturating the DER and enhancing the voltage profile. The proposed DCA is independent of the design of the control unit, does not require parameter tuning, exchanges only the regulation error at a low sampling rate, handles multiple saturations, and has limited communication requirements. The effectiveness of the proposed DCA is validated through numerical simulations of an ADN composed of two IEEE 13-bus Test Feeders. Full article

The proactive involvement of photovoltaic (PV) smart inverters (PVSIs) in grid management facilitates voltage regulation and enhances the integration of distributed energy resources (DERs) within distribution networks. However, to fully exploit the capabilities of PVSIs, it is essential to achieve optimal control of their operations and effective coordination with voltage-regulating devices in the distribution network. This study developed a dual strategy approach to forecast the optimal setpoints of onload tap changers (OLTCs), PVSIs, and distribution static synchronous compensators (DSTATCOMs) to improve the voltage profiles in power distribution systems. The study began by running a centralized AC optimal power flow (CACOPF) and using the hourly PV output power and the load demand to determine the optimal active and reactive power of the PVSIs, the setpoint of the DSTATCOM, and the optimal tap setting of the OLTC. Furthermore, Machine Learning (ML) models were trained as controllers to determine the reactive-power setpoints for the PVSIs and DSTATCOMs as well as the optimal OLTC tap position required for voltage stability in the network. To assess the effectiveness of the method, comprehensive evaluations were carried out on a modified IEEE 33 bus with a high penetration of PV energy. The results showed that deep neural networks (DNNs) outperformed other ML models used to mimic the coordination method based on CACOPF. Furthermore, when the DNN-based controller was tested and compared with the optimizer approach under different loading and PV conditions, the DNN-based controller was found to outperform the optimizer in terms of computational time. This approach allows predictive control in power systems, helping system operators determine the action to be initiated under uncertain PV energy and loading conditions. The approach also addresses the computational inefficiency arising from contingencies in the power system that may occur when optimal power flow (OPF) is run multiple times. Full article

The stable control of the main steam flow rate (MSFR) can effectively improve the waste combustion efficiency and energy utilization, reduce environmental pollution, and is crucial for promoting the sustainable development of municipal solid waste incineration (MSWI). Developed countries benefit from stable municipal solid waste (MSW) composition, enabling advanced automated combustion control. However, in developing countries, fluctuating waste composition and calorific value cause frequent disturbances, limiting the use of foreign control methods. Therefore, MSFR control technologies suited to developing countries are crucial. This study proposes a two-layer intelligent control method, consisting of an optimization setting layer and a loop control layer. The optimization layer uses a steam flow prediction model (OPTICS and RBF) and an improved antlion optimizer (IALO) for manipulated variable setpoints. The control layer applies reinforcement learning (actor–critic) to fine-tune PI controller parameters. Experimental results show that the proposed method adaptively adjusts manipulated variables, ensuring MSFR control within the target range and maintaining efficient, stable MSWI operation. Full article

The integration of battery energy storage systems (BESSs) within active distribution networks (ADNs) entails optimized day-ahead charge/discharge scheduling to achieve effective peak shaving.The primary objective is to reduce peak demand and mitigate power deviations caused by intermittent photovoltaic (PV) output. Quasi-static time-series (QSTS) co-simulations for determining optimal heuristic solutions at each time interval are computationally intensive, particularly for large-scale systems. To address this, a two-stage intelligent BESS scheduling approach implemented in a MATLAB–OpenDSS environment with parallel processing is proposed in this paper. In the first stage, a rule-based decision tree generates initial charge/discharge setpoints for community BESS units. These setpoints are refined in the second stage using an optimization algorithm aimed at minimizing community net load power deviations and reducing peak demand. By assigning each ADN community to a dedicated CPU core, the proposed approach utilizes parallel processing to significantly reduce the execution time. Performance evaluations on an IEEE 8500-node test feeder demonstrate that the approach enhances peak shaving while reducing QSTS co-simulation execution time, utility peak demand, distribution network losses, and point of interconnection (POI) nodal voltage deviations. In addition, the use of smart inverter functions improves BESS operations by mitigating voltage violations and active power curtailment, thereby increasing the amount of energy shaved during peak demand periods. Full article

This study proposes an enhanced derivative control strategy, named $PI$-$D^{\AE }$, designed to overcome key limitations of the derivative ($D$) term, such as noise amplification, derivative kick $(D$-$k)$, and tuning difficulties. These issues often arise in high-frequency or rapidly changing systems, in which traditional PID controllers struggle. The proposed solution introduces a novel adaptive exponent algorithm (Æ) that dynamically modulates the $D$ term based on the evolving relationship between system output and setpoint. This yields the $PI$-$D^{\AE }$ controller, which adapts in real time to changing conditions. The results show significant performance improvements. Simulation results on two systems demonstrate that $PI$-$D^{\AE }$ achieves a 90% faster response time, a 35% reduction in peak time, and a 100% improvement in settling time compared with conventional PID controllers, all while maintaining a near-zero steady-state error even under external disturbances. Unlike more-complex alternatives such as fuzzy logic, neural networks, or sliding mode control, $PI$-$D^{\AE }$ retains the simplicity and robustness of PID, avoiding high computational costs or intricate setups. This adaptive exponent strategy offers a practical and scalable enhancement to classical PID, improving performance and robustness without added complexity, and thus provides a promising control solution for real-world applications in which simplicity, adaptability, and reliability are essential. Full article

This research study proposes an indoor temperature regulation predictive optimal control system that entails the use of both deep reinforcement learning and the Modbus TCP communication protocol. The designed architecture comprises distributed sub-parts, namely, distributed room-level units as well as a centralized main-part AI controller for maximizing efficient HVAC management in single-family residences as well as small-sized buildings. The system utilizes an LSTM model for forecasting temperature trends as well as an optimized control action using an envisaged DQN with predicted states, sensors, as well as user preferences. InfluxDB is utilized for gathering real-time environmental data such as temperature and humidity, as well as consumed power, and storing it. The AI controller processes these data to infer control commands for energy efficiency as well as thermal comfort. Experimentation on an NVIDIA Jetson Orin Nano as well as on a Raspberry Pi 4 proved the efficacy of the system, utilizing 8761 data points gathered hourly over 2023 in Cheonan, Korea. An added hysteresis-based mechanism for controlling power was incorporated to limit device wear resulting from repeated switching. Results indicate that the AI-based control system closely maintains target temperature setpoints with negligible deviations, affirming that it is a scalable, cost-efficient solution for intelligent climate management in buildings. Full article

In this paper, a modification of the classical PID controller scheme for position control is presented. The resulting controller incorporates an exponential trajectory that smoothly guides the system towards the setpoint and a hybrid mechanism to dynamically reset the exponential signal, allowing an adaptive response to discontinuous reference signals. This combination leverages the benefits of exponential trajectories to reduce overshoot and transient oscillations, while the hybrid system ensures robust performance over a wide range of operating scenarios. Among the advantages of the proposed approach, two stand out: (1) significant improvements in energy savings can be achieved in some cases, and (2) closed-loop system performance can be improved even considering poorly tuned PIDs. Full article

This paper advances two interrelated theses. As for the first thesis, it distinguishes well-being, on the one hand, from happiness, on the other hand. As for the second thesis, it differentiates between two important facets of happiness: what this paper calls “happiness-as-tranquility” and “happiness-as-aspiration”. Actually, in order to differentiate the two facets of happiness, we first need to distinguish happiness from well-being. This is the case because happiness, after all, is a by-product of reflecting upon and ruminating over well-being. Given it is the same well-being, how could it give rise to different facets of happiness? It can only do so if we stop conflating happiness with well-being. This entails taking to task the widely accepted concept of “subjective wellbeing”. Such concept is expressly designed to obfuscate the difference between well-being and happiness. As for the two facets of happiness (the second thesis), this paper relies upon the contrast of two famous works of literature: the story of Job and the story of Faust. The contrast uncovers the criticality of the temporal dimension in the acts of reflection upon and rumination over well-being. If people reflect on past accomplishments, they experience backward-looking happiness along the Job story—i.e., happiness-as-tranquility. If people reflect on desire, they experience forward-looking happiness along the Faust story—i.e., happiness-as-aspiration. While the two facets of happiness seem contradictory, they are indeed complementary if we recognize the temporal element when one reflects upon and ruminates over well-being. Full article

In the context of global warming, building transformation takes on a dual responsibility to be more energy-efficient and sustainable for climate change mitigation and to be more climate-resilient for occupants’ comfort. The building energy retrofitting is an urgent need due to the large amount of existing building stock. Especially in high-rise and high-density cities under a subtropical climate, like Hong Kong, existing buildings with large glazed façades face the challenges of high energy consumption and overheating risks. An advanced glazing system, namely the vacuum insulating glazing (VIG), shows the potential for effective building envelope retrofitting due to its excellent thermal insulation ability. Yet, its performance for practical applications in the subtropical region has not been investigated. To enhance the energy performance and thermal comfort of existing high-rise buildings, this study proposed a novel retrofitting approach by integrating the VIG into the existing window system as secondary glazing. Field experiments were conducted in a commercial building in Hong Kong to investigate the thermal performance of the VIG retrofit application under real-world conditions. Furthermore, the energy-saving potential and thermal comfort performance of the VIG retrofit were evaluated by building energy simulations. The experimental results indicate that the VIG retrofit can effectively stabilize the fluctuation of the inside glass surface temperature and significantly reduce the heat gain by up to 85.3%. The simulation work shows the significant energy-saving potential of the VIG retrofit in Hong Kong. For the VIG retrofit cases under different scenarios, the energy-saving potential varies from 12.5% to 29.7%. In terms of occupants’ thermal comfort, the VIG retrofit can significantly reduce the overheating risk and improve thermal satisfaction by 9.2%. Due to the thermal comfort improvement, the cooling setpoint could be reset to 1 °C higher without compromising the overall thermal comfort. The average payback period for the VIG application is 5.8 years and 8.6 years for the clear glass retrofit and the coated glass retrofit, respectively. Therefore, the VIG retrofit approach provides a promising solution for building envelope retrofits under subtropical climate conditions. It not only benefits building owners and occupants but also contributes to achieving long-term climate resilience and the carbon neutrality of urban areas. Full article

COVID-19 restricted the number of employees. Operational data showed that traditional methods of modeling heat consumption are not correct anymore. The aim is to model the energy demand of an office building during COVID-19 limitations and showcase improvements after a new controller or suggested alternatives are applied. After an actual heat consumption profile was simulated, energy conservation scenarios were considered: the usage of thermostatic radiator valves (TRVs); accounting impacts of solar radiation and wind; changing mass flow rates based on the indoor temperature; adopting an additional control, changing the temperature setpoint; introducing night and day setbacks. After implementing new design and operational methods, the overheating of indoor spaces was alleviated, and the average indoor temperature was reduced from 23.5 °C to 20.4 °C. The annual specific heat consumption decreased to 174 kWh/m² (20.2% lower). The methodology ensured thermal comfort and high energy-saving potential. If operating parameters were adjusted, the total saving effect in energy demand was 119.8 MWh, with an energy-saving rate of 19.8%. Employing TRV-related savings and considering thermal inertia provided more stable indoor temperatures and higher energy performance. The minimum saving effect corresponded to the optimal operation and ensuring the indoor environment by considering wind and the maximum one-to-night setbacks. The fluctuations in indoor temperature became smoother. Full article

When large-scale and clustered distributed photovoltaic devices are connected to an active distribution network, the safe and stable operation of the distribution network is seriously threatened, and it is difficult to satisfy the demand for preservation of supply. Multi-agent reinforcement learning provides an idea of zonal balance control, but it is difficult to fully satisfy operation constraints. In this paper, a multi-level control framework based on a local physical model and a multi-agent sequential update algorithm is proposed. The framework generates parameters through an upper-layer reinforcement learning algorithm, which are passed into the objective function of the lower-layer local physical model. The lower-layer local physical model will incorporate safety constraints to determine the power setpoints of the devices; meanwhile, the sequential updating algorithm is integrated into a centralized training–decentralized execution framework, which can increase the efficiency of the sample utilization and promote the monotonic improvement of the strategies. The modified 10 kV IEEE 69-node system is studied as an example, and the results show that the proposed framework can effectively reduce the total operating cost of the active distribution network, while meeting the demand of the system to preserve the supply and ensure the safe and stable operation of the system. Full article

To improve the dynamic response, linearity, and control accuracy of the YYSCT-250-3 tractor multi-circuit hydraulic output power tester, this study develops a particle swarm optimization (PSO)-tuned fuzzy-proportional–integral–derivative (Fuzzy-PID) control strategy. By modulating the actuator-driven ball valve’s rotation angle (0–90°) in the proportional flow valve, the controller uses both the flow rate error and its rate of change between the setpoint and the flow meter feedback as fuzzy inputs to adjust the PID outputs. A detailed mathematical model of the electro-hydraulic proportional flow system is established, incorporating hydraulic resistance torque on the ball valve spool and friction coefficients to enhance accuracy. Through MATLAB/Simulink (R2022a) simulations, the PSO algorithm optimizes the fuzzy membership functions and PID gains, yielding faster response, reduced overshoot, and minimal steady-state error. The optimized controller achieved relative steady-state flow errors within ±1.0% and absolute flow control errors within ±0.5 L/min, significantly outperforming the traditional PID controller. These results demonstrate that the PSO-optimized Fuzzy-PID approach effectively addresses the flow control challenges of the YYSCT-250-3, enhancing both testing efficiency and precision. This work provides a robust theoretical framework and practical reference for rapid, high-precision flow control in multi-channel hydraulic power testing. Full article

The integration of new technologies in Industry 4.0 has modernised agriculture, fostering the concept of smart agriculture (Agriculture 4.0). Higher education institutions are incorporating digital technologies into agricultural curricula, equipping students in agriculture, agronomy, and engineering with essential skills. The implementation of targeted STEM activities has the potential to enhance the teaching of Agriculture 4.0 through the utilisation of practical applications that stimulate student interest, thereby facilitating more accessible and effective teaching. In this context, this study presents a system comprising retrofitted real-scale components that facilitate the understanding of digital technologies and automations in agriculture. The specific system utilises a typical centrifugal electric pump and a water tank and adds logic to it, so that its flow follows various user-defined setpoints, given and monitored via a smartphone application, despite the in-purpose disturbances invoked via intermediating valves. This setup aims for students to gain familiarity with concepts such as closed-loop systems and PID controllers. Going further, fertile ground is provided for experimentation on the efficiency of the PID controller via testing different algorithmic variants incorporating non-linear methods as well. Feedback collected from the participating students via a corresponding survey highlights the importance of integrating similar hands-on interdisciplinary activities into university curricula to foster engineering education. Full article

Three factors that complicate the position and orientation control of unmanned autonomous vehicles are the hardware-induced delays, delays caused by teleoperated modes of operation, and persistent disturbances such as wind. In this article, we study how to overcome these challenges by controlling a twin rotor multi-input, multi-output system. This nonlinear system has two degrees of freedom and significant cross-coupling, making its control particularly challenging. Time delay and external disturbances contribute to system instability and complicate flight control. The effects of time delay can be mitigated using the standard Smith predictor controller. However, this approach performs poorly when persistent disturbances are present. Our contribution is a modified Smith predictor that effectively mitigates disturbances. Additionally, a decoupler is employed to transform the system into two single-input, single-output models, enabling precise control of both the horizontal and vertical planes. The proposed method is implemented on a prototype and compared with the original Smith predictor-based control scheme. Experimental results validate the effectiveness of the proposed approach, showing that our approach reduces the steady-state tracking error to less than 2%, decreases settling time from 30 s to 20 s, and improves disturbance rejection under wind disturbance conditions by 35% compared to the standard Smith predictor. These results demonstrate the superior set-point tracking performance and enhanced disturbance rejection of the proposed approach. Full article