Search Results (311)

To address the vulnerability of single-polarization deception jamming and simply modulated dual-polarization jamming to discrimination by dual-polarization radars, this paper proposes a deception jamming method based on joint fast–slow-time polarization modulation (FSPMJ). First, in the slow-time domain (across multiple pulses), the polarization azimuth of the jamming signal is designed according to the target’s polarization ratio distribution. Subsequently, with the target polarization degree as the optimization objective, the polarization phase difference of the jamming signal is solved using an interior-point optimization algorithm, establishing the initial polarization state for each pulse. This process is iterated to design the polarization state for the first half of each pulse. Then, in the fast-time domain (within a single pulse), a polarization state orthogonal to the pre-generated first-half state, is constructed to serve as the polarization state for the latter half of each pulse. Finally, the effectiveness of the proposed method is validated through combined simulation and measured data using a Support Vector Machine (SVM) algorithm. Results demonstrate that compared to single-polarization deception jamming and existing polarization-modulated jamming, this method reduces the false target discrimination rate of dual-polarization radars by 35.4% without requiring complex target scattering matrices. Full article

This article presents an interaction-aware motion planning framework that integrates a graph neural network (GNN) based multi-modal trajectory predictor with a model predictive control (MPC) based planner. Unlike past studies that predict a single future trajectory per agent, our algorithm outputs three distinct trajectories for each surrounding road user, capturing different interaction scenarios (e.g., yielding, non-yielding, and aggressive driving behaviors). We design a GNN-based predictor with bi-directional gated recurrent unit (Bi-GRU) encoders for agent histories, VectorNet-based lane encoding for map context, an interaction-aware attention mechanism, and multi-head decoders to predict trajectories for each mode. The MPC-based planner employs a bicycle model and solves a constrained optimal control problem using CasADi and IPOPT (Interior Point OPTimizer). All three predicted trajectories per agent are fed to the planner; the primary prediction is thus enforced as a hard safety constraint, while the alternative trajectories are treated as soft constraints via penalty slack variables. The designed motion planning algorithm is examined in real-world intersection scenarios from the INTERACTION dataset. Results show that the multi-modal trajectory predictor covers possible interaction outcomes, and the planner produces smoother and safer trajectories compared to a single-trajectory baseline. In high-conflict situations, the multi-modal trajectory predictor anticipates potential aggressive behaviors of other drivers, reducing harsh braking and maintaining safe distances. The innovative method by integrating the GNN-based multi-modal trajectory predictor with the MPC-based planner is the backbone of the effective motion planning algorithm for robust, safe, and comfortable autonomous driving in complex intersections. Full article

This study proposes a current control strategy to enhance the control stability of an interior permanent magnet synchronous motor (IPMSM) under transient conditions, such as rapid acceleration or deceleration in electric vehicle (EV) applications. Conventional current control methods provide optimal steady-state current references corresponding to torque commands using a lookup table (LUT)-based approach. However, during transitions between these reference points, particularly in the field-weakening region at high speeds, the voltage limit may be exceeded. When the voltage limit is exceeded, unstable overmodulation states may occur, degrading stability and resulting in overshoot of the inverter input current. Although ramp generators are commonly employed to interpolate between current references, a fixed ramp slope may fail to ensure a sufficient voltage margin during rapid transients. In this study, a method is proposed to dynamically adjust the rate of change of the d-axis current reference in real time based on the difference between the inverter output voltage and its voltage limit. By enabling timely field-weakening before rapid changes in speed or q-axis current, the proposed strategy maintains control stability within the voltage limit. The effectiveness of the proposed method was verified through simulations based on real vehicle driving profiles and dynamometer experiments using a 38 kW class IPMSM for a hybrid electric vehicle (HEV), demonstrating reduced input DC current overshoot, improved voltage stability, and enhanced torque tracking performance under high-speed transient conditions. Full article

This study proposes an integrated computational framework that combines deep learning-based visual perception analysis with multi-criteria decision making to optimize indoor architectural layouts in terms of both visual coherence and sustainability. The framework initially employs a deep learning method leveraging edge pixel feature recombination to extract critical spatial layout features and determine key visual focal points. A fusion model is then constructed to preprocess visual representations of interior layouts. Subsequently, an evolutionary deep learning algorithm is adopted to optimize parameter convergence and enhance feature extraction accuracy. To support comprehensive evaluation and decision making, an improved Analytic Hierarchy Process (AHP) is integrated with the entropy weight method, enabling the fusion of objective, data-driven weights with subjective expert judgments. This dual-focus framework addresses two pressing challenges in architectural optimization: sensitivity to building-specific spatial features and the traditional disconnect between perceptual analysis and sustainability metrics. Experimental results on a dataset of 25,400 building images demonstrate that the proposed method achieves a feature detection accuracy of 92.3%, surpassing CNN (73.6%), RNN (68.2%), and LSTM (75.1%) baselines, while reducing the processing time to under 0.95 s and lowering the carbon footprint to 17.8% of conventional methods. These findings underscore the effectiveness and practicality of the proposed model in facilitating intelligent, sustainable architectural design. Full article

This paper presents a hybrid optimization framework for solving the optimal reactive power compensation problem in medium-voltage smart distribution networks. Leveraging Julia’s computational environment, the proposed method combines the global search capabilities of the Chu & Beasley genetic algorithm (CBGA) with the local refinement efficiency of the interior-point optimizer (IPOPT). The objective is to minimize the annualized operating costs by reducing active power losses while considering the investment and operating costs associated with thyristor-switched capacitors (TSCs). A key contribution of this work is the comparative assessment of fixed and time-varying reactive power injection strategies. Simulation results on the IEEE 33- and 69-bus test feeders demonstrate that the proposed CBGA-IPOPT framework achieves annualized cost reductions of up to 11.22% and 12.58% (respectively) under fixed injection conditions. With variable injection, cost savings increase to 12.43% and 14.08%. A time-domain analysis confirms improved voltage regulation, substation reactive demand reductions exceeding 500 kvar, and peak loss reductions of up to 32% compared to the uncompensated case. Benchmarking shows that the hybrid framework not only consistently outperforms state-of-the-art metaheuristics (the sine-cosine algorithm, the particle swarm optimizer, the black widow optimizer, and the artificial hummingbird algorithm) in terms of solution quality but also demonstrates high solution repeatability across multiple runs, underscoring its robustness. The proposed method is directly applicable to real-world distribution systems, offering a scalable and cost-effective solution for reactive power planning in smart grids. Full article

Efficient autonomous exploration in unknown obstacle cluttered environments with interior obstacles remains a challenging task for mobile robots. In this work, we present a novel exploration process for a non-holonomic agent exploring 2D spaces using onboard LiDAR sensing. The proposed method generates velocity commands based on the calculation of the solution of an elliptic Partial Differential Equation with Dirichlet boundary conditions. While solving Laplace’s equation yields collision-free motion towards the free space boundary, the agent may become trapped in regions distant from free frontiers, where the potential field becomes almost flat, and consequently the agent’s velocity nullifies as the gradient vanishes. To address this, we solve a Poisson equation, introducing a source point on the free explored boundary which is located at the closest point from the agent and attracts it towards unexplored regions. The source values are determined by an exponential function based on the shortest path of a Hybrid Visibility Graph, a graph that models the explored space and connects obstacle regions via minimum-length edges. The computational process we apply is based on the Walking on Sphere algorithm, a method that employs Brownian motion and Monte Carlo Integration and ensures efficient calculation. We validate the approach using a real-world platform; an AmigoBot equipped with a LiDAR sensor, controlled via a ROS-MATLAB interface. Experimental results demonstrate that the proposed method provides smooth and deadlock-free navigation in complex, cluttered environments, highlighting its potential for robust autonomous exploration in unknown indoor spaces. Full article

Industrial energy systems are increasingly required to reduce operating costs and CO₂ emissions while integrating variable renewable energy sources. Managing these objectives under uncertainty requires advanced optimization strategies capable of delivering reliable and real-time decisions. To address these challenges, this study focuses on the short-term operational planning of an industrial energy supply system using the rolling horizon approach (RHA). The RHA offers an effective framework to handle uncertainties by repeatedly updating forecasts and re-optimizing over a moving time window, thereby enabling adaptive and responsive energy management. To solve the resulting nonlinear and constrained optimization problem at each RHA iteration, we propose a novel hybrid algorithm that combines Bayesian optimization (BO) with the Interior Point OPTimizer (IPOPT). While global deterministic and stochastic optimization methods are frequently used in practice, they often suffer from high computational costs and slow convergence, particularly when applied to large-scale, nonlinear problems with complex constraints. To overcome these limitations, we employ the BO–IPOPT, integrating the global search capabilities of BO with the efficient local convergence and constraint fulfillment of the IPOPT. Applied to a large-scale real-world case study of a food and cosmetic industry in Germany, the proposed BO–IPOPT method outperformed state-of-the-art solvers in both solution quality and robustness, achieving up to 97.25%-better objective function values at the same CPU time. Additionally, the influence of key parameters, such as forecast uncertainty, optimization horizon length, and computational effort per RHA iteration, was analyzed to assess their impact on system performance and decision quality. Full article

The natural gas pipeline network transmission system involved in the coordinated operation of pipeline networks and gas-fired power generation facilities is complex. It consists of multiple components, such as gas sources, users, valves, compressor stations, and pipelines. The addition of natural gas-fired power generation facilities overlaps with the high and low peak periods of civil gas, imposing dual peak-shaving pressures on pipeline networks and requiring more stringent operational control strategies for maintaining system stability. To address the aforementioned issues and improve the overall operating revenues of the system, we proposed the coordinated optimization model of gas-fired power generation facilities, pipeline networks, gas storage, and compressor stations. The optimization algorithm is written using the penalty function method of the Interior Point OPTimizer (IPOPT) solver. Meanwhile, the basic parameters of the system’s pipeline networks, users, gas storage, natural gas-fired power generation facilities, compressors, and electricity prices were input into the solver. The research results reveal that the algorithm ensures solution accuracy while accounting for computational efficiency and practical applicability. The algorithm can be used to effectively calculate the ideal coordinated operation solution, significantly improve the operating revenues of the system, and achieve safe, stable, coordinated, and efficient operation of the system. Full article

This research presents a method for improving the regenerative efficiency of interior permanent magnet synchronous motors (IPMSMs) used in traction applications such as electric vehicles. In conventional powertrain control, the maximum torque per ampere (MTPA) strategy is commonly applied in the constant-torque region. However, this approach does not account for the combined losses of both the motor and inverter. In this study, overall system efficiency is investigated, and an improved current combination is proposed to minimize total losses. The single switching method is employed in the inverter due to its simplicity and its ability to reduce inverter losses. Simulations incorporating both motor and inverter losses were performed for two driving conditions around the MTPA current point. The results show that the optimal current combination slightly deviates from the MTPA point and leads to a slight improvement in efficiency. Experimental results under the two steady-state driving torque and angular velocity conditions confirm that the optimized current combination enhances system efficiency. Furthermore, simulations based on the Urban Dynamometer Driving Schedule predict an increase in recovered energy of approximately 1%. The proposed control strategy is simple, easy to implement, and enables the powertrain to operate with highly efficient current references. Full article

This study presents a sustainable and adaptive approach to mineral processing. A hybrid intelligent control system was developed to beneficiate fine chromite ore in a jigging machine. The objective is to enhance separation efficiency and reduce chromium losses through real-time optimization of process parameters under variable feed conditions. The method addresses ore composition fluctuations by integrating three components: Physical modeling of particle motion, regression analysis, and neural network-based prediction. The jig bed level and pulsation frequency are used as control variables, while the Cr₂O₃ content in the feed ($C_r$) is treated as a disturbance. A neural network predicts the Cr₂O₃ content in the concentrate ($C_c$) and in the tailings ($C_t$), representing chromite-rich and gangue fractions, respectively. The optimization is performed using a constrained Interior-Point algorithm. The model demonstrates high predictive accuracy, with a mean squared error (MSE) below 0.01. The proposed control algorithm reduces chromium losses in tailings from 7.5% to 5.5%, while improving concentrate quality by 3–6%. A real-time human–machine interface (HMI) was developed in SIMATIC WinCC for process visualization and control. The hybrid framework can be adapted to other mineral processing systems by adjusting the model structure and retraining the neural network on new ore datasets. Full article

With accelerating urbanization, the outdoor thermal environment has become a critical factor affecting the thermal comfort of public spaces, particularly in high-density commercial districts and pedestrian-concentrated areas. To enhance thermal comfort and livability in public outdoor space, this study proposes a thermal demand-responsive design approach that integrates thermal conditions with pedestrian flow dynamics. A commercial pedestrian mall featuring semi-open public spaces and air-conditioned interior retail areas was selected as a case study. Computational Fluid Dynamics (CFD) simulations were conducted based on design-phase documentation and field measurements to model the thermal environment. The Universal Thermal Climate Index (UTCI) was employed to assess thermal comfort levels, and thermal discomfort was further quantified using the Heat Discomfort Index (HI). Simultaneously, pedestrian density distribution (λ) was analyzed using the agent-based simulation software MassMotion (Version 11.0). A demand of thermal comfort (DTC) index was developed by coupling UTCI-based thermal conditions with pedestrian density, enabling the spatial quantification of thermal demand across the whole commercial pedestrian mall. For example, in a sidewalk area parallel to the main street, several points exhibited high discomfort levels (HI = 0.95) but low pedestrian volume, resulting in DTC values approximately 0.2 units lower than adjacent zones with lower discomfort levels (HI = 0.7) but higher foot traffic. Such differences demonstrate how DTC can reveal priority areas for intervention. Key zones requiring thermal improvement were identified based on DTC values, providing a quantitative foundation for outdoor thermal environment design. This method provides both a theoretical foundation and a practical tool for the sustainable planning and optimization of urban public spaces. Full article

Diffusiophoresis of a weakly charged dielectric droplet in a cylindrical pore is investigated theoretically in this study. The governing fundamental electrokinetic equations are solved with a patched pseudo-spectral method based on Chebyshev polynomials, coupled with a geometric mapping scheme to take care of the irregular solution domain. The impact of the boundary confinement effect upon the droplet motion is explored in detail, which is most profound in narrow channels. We found, among other things, that the droplet moving direction may reverse with varying channel widths. Enhanced motion-inducing double-layer polarization due to the presence of a nearby channel wall is found to be responsible for it. In particular, an interesting and seemingly peculiar phenomenon referred to as the “solidification phenomenon” is observed here at some specific critical droplet sizes or electrolyte strengths in narrow channels, under which all the droplets move at identical speeds regardless of their viscosities. They move like a rigid particle without the surface spinning motions and the induced interior recirculating vortex flows. As the corresponding shear rate is zero at this point, the droplet is resilient to undesirable exterior shear stresses tending to damage the droplet in motion. This provides a helpful guideline in the fabrication of liposomes in drug delivery in terms of the optimal liposome size, as well as in the microfluidic and nanofluidic manipulations of cells, among other potential practical applications. The effects of other parameters of electrokinetic interest are also examined. Full article

This paper proposes a high-efficiency design for an external rotor interior permanent magnet synchronous motor (IPMSM) that eliminates the magnetic leakage flux path. The conventional model based on an external rotor surface-mounted permanent magnet synchronous motor (SPMSM) is analyzed using a statistical method. Design directions are derived by comparing efficiencies at two major operating points with different motor characteristics. A V-shaped IPMSM is then proposed to increase the permanent magnet volume and reduce magnetic leakage. Design optimization is conducted using Gaussian process models (GPMs) constructed with a Latin hypercube design (LHD), and the optimal design is determined using a gradient descent algorithm. A prototype is fabricated to confirm manufacturability, and the improved efficiency of the proposed design is experimentally verified. The results demonstrate that the proposed IPMSM significantly outperforms the conventional SPMSM in terms of efficiency across both operating points. Full article

The optimal selection of conductors (OCS) in radial distribution networks is a critical aspect of system planning, directly impacting both investment costs and energy losses. This paper proposed a mixed-integer convex (MI-Convex) optimization framework to solve the OCS problem under balanced operating conditions, integrating the costs of conductor investment and energy losses into a single convex objective. This formulation leveraged second-order conic constraints and was solved using a combination of branch-and-bound and interior-point methods. Numerical validations on standard 27-, 33-, and 85-bus test systems confirmed the effectiveness of the proposal. In the 27-bus grid, the MI-Convex approach achieved a total cost of $550,680.25, outperforming or matching the best results reported by state-of-the-art metaheuristic algorithms, including the vortex search algorithm (VSA), Newton’s metaheuristic algorithm (NMA), the generalized normal distribution optimizer (GNDO), and the tabu search algorithm (TSA). The MI-Convex method demonstrated consistent and repeatable results, in contrast to the variability observed in heuristic techniques. Further analyses considering three-period and daily load profiles led to cost reductions of up to 27.6%, and incorporating distributed renewable generation into the 85-bus system achieved a total cost of $705,197.06—approximately 22.97% lower than under peak-load planning. Moreover, the methodology proved computationally efficient, requiring only 1.84 s for the 27-bus and 12.27 s for the peak scenario of the 85-bus. These results demonstrate the superiority of the MI-Convex approach in achieving globally optimal, reproducible, and computationally tractable solutions for cost-effective conductor selection. Full article

Recently, there has been an increasing interest in sustainable design to improve and treat interior space to reduce pollution and protect the environment. Given the global trend towards increasing the ability of interior design elements to adapt to environmental conditions to improve the efficiency of interior spaces, we found it important to conduct a study on the development of interior design for classrooms in the Art Education Department at the College of Education at King Faisal University. This study aimed to reveal the reality of classrooms in the Art Education Department and reach a proposed vision for designing classrooms to achieve environmental sustainability in the Art Education Department at King Faisal University. This study used the descriptive survey method and the descriptive analytical method due to their suitability for this study. The study sample consisted of approximately 100% of faculty members and 100% of students in the Art Education Department at King Faisal University. The results showed that the design standards required to be achieved to create environmental sustainability in the classrooms of the Art Education Department at King Faisal University from the point of view of faculty members and students were only slightly appropriate, and there was a noticeable agreement by the arbitrators specialized in the field of design and arts that the proposed concept for designing classrooms to achieve environmental sustainability was highly acceptable. Full article