https://jst.vn/index.php/ssad Journal of Science and Technology - Technical Universities Hanoi University of Science and Technology en-US Smart Systems and Devices 3093-3285 https://jst.vn/index.php/ssad/article/view/1043 The performance and reliability of industrial control systems are impacted by external influences such as fluctuating operating environments and disruptive interferences, presenting notable challenges. Consequently, the exploration and adoption of smart control algorithms with capabilities for autonomous learning, self-tuning, and self-adjustment have emerged as a vital and significant research focus. This research investigates an intelligent control technique that employs the Deep Deterministic Policy Gradient (DDPG) algorithm for process control systems with a dual-tank system selected as the case study, in which the flow rate is manipulated to regulate the system temperature. The performance of the observer component in the critic network is improved by integrating a densely connected layer, which enhances its capacity to represent and handle data, thereby improving the identification of essential characteristics for water-level management. Additionally, the neural network’s node settings are fine-tuned, and the ReLU activation function is implemented to support ongoing monitoring and adaptation to the external tank environment while preventing gradient vanishing. The research firstly trains DDPG with various initial conditions and then validates the performance for the temperature control problem by simulation. Additionally, the performance of DDPG is compared to the conventional Proportional-Integral-Derivative (PID) controller in terms of rise time, settling time, overshoot, and steady-state error. Ms Anh Nguyen Minh Mr Lam Tran Tung Mr Dat Lai Quoc Mr Lam Hoang Phuc Dr Ha Nguyen Thu Copyright (c) 2026 Smart Systems and Devices https://creativecommons.org/licenses/by-nc/4.0/ 2026-03-10 2026-03-10 36 3 001 009 10.51316/jst.192.ssad.2026.36.3.1 https://jst.vn/index.php/ssad/article/view/1228 The integration of renewable energy sources in microgrids introduces significant operational challenges due to their intermittent nature and the mismatch between generation and demand patterns. Effective demand response (DR) strategies are crucial for maintaining system stability and economic efficiency, particularly in microgrids with high renewable penetration. This paper presents a comprehensive mixed-integer linear programming (MILP) framework for optimizing DR operations in a microgrid with solar generation and battery storage systems. The framework incorporates load classification, dynamic price thresholding, and multi-period coordination for optimal DR event scheduling. Analysis across seven distinct operational scenarios demonstrates peak load reduction of 5–10% while achieving energy cost savings ranging from 7.5% to 24.5%. The highest performance was observed in scenarios with high solar generation, where the framework achieved 24.49% energy cost reduction through optimal coordination of renewable resources and DR actions. The results validate the framework’s effectiveness in managing diverse operational challenges while maintaining system stability and economic efficiency. Mr. Quan Le Anh Mr. Thanh Trinh Xuan Mr. Anh Nguyen Tuan Mr. Vinh Pham Thanh Mr. Hung Ta Xuan Assoc. Prof. Tuyen Nguyen Duc Dr. Son Tran Thanh Copyright (c) 2026 Smart Systems and Devices https://creativecommons.org/licenses/by-nc/4.0/ 2026-06-25 2026-06-25 36 3 010 018 10.51316/jst.192.ssad.2026.36.3.2 https://jst.vn/index.php/ssad/article/view/1619 A microgrid-connected electric vehicle charging cluster with photovoltaic (PV) generation, battery energy storage systems (BESS) and local energy sharing is evaluated using a proposed techno-economic framework. The suggested system studies the impact of local PV generation, stationary storage and internal energy exchange on grid import, energy export, PV utilization and operating cost. The BESS improves the dispatchability of variable PV generation by storing excess energy and supplying charging demand in the high-price or high-load periods. Internal energy trading allows the surplus energy of hybrid charging stations to support the deficit stations in the vicinity before interacting with the utility grid. Simulation results show that the coordinated operation can reduce dependence on the grid, limit the export of low-value energy, improve the utilization of local renewable energy and reduce the overall operating cost of the charging cluster. The proposed framework therefore provides a practical basis for assessing the economic and technical feasibility of renewable-assisted EV charging cluster sunder different solar, storage, and tariff conditions. Muhammad Adnan Myungchin Kim Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0 2026-09-14 2026-09-14 36 3 019 026 10.51316/jst.192.ssad.2026.36.3.3 https://jst.vn/index.php/ssad/article/view/1537 Distribution system planning increasingly requires integrated approaches that connect building-level renewable energy potential, household affordability, and grid infrastructure priorities within a unified and reproducible framework. However, rooftop solar suitability studies typically focus only on technical potential, energy equity assessments rarely inform grid planning, and grid asset prioritization often relies primarily on connected customer counts. This paper proposes a two-stage geographic information system (GIS) framework that integrates rooftop solar suitability, socioeconomic affordability, and distribution grid prioritization. In Stage 1, eligible buildings are evaluated using a technical suitability score incorporating regional solar resources, usable rooftop area, and the ratio of modeled photovoltaic (PV) generation to electricity demand. Neighborhood-level affordability indicators derived from the American Community Survey are then used to determine appropriate solar deployment pathways rather than excluding economically constrained households. In Stage 2, PV generation is incorporated into building-level residual demand, which is spatially aggregated to candidate substations to reassess their relative planning priorities. The framework is demonstrated using five U.S. Census ZIP Code Tabulation Areas in southeastern Michigan. After data deduplication and boundary validation, 63,187 unique building records were identified, of which 22,445 met the required building classification, footprint, and socioeconomic data criteria. Under the base scenario, 1,891 buildings were assigned a green energy pathway, reducing modeled annual electricity demand from 1.031 TWh to 0.815 TWh, corresponding to a 20.93% reduction. Among 1,610 technically suitable residential buildings, 889 (55.2%) were directed toward community solar or financing-supported adoption based on neighborhood affordability. Incorporating distributed PV also altered grid planning priorities: 24 of 36 candidate substations changed rank and five changed priority class. Across sensitivity scenarios, modeled demand reductions ranged from 0.60% to 52.79%. Mr Atharva Gujarathi Mr Sahil Manikshete Mr Vikram Velankar Mr Prasidh Shetty Asst. Prof. Hai Bui Van Copyright (c) 2026 Smart Systems and Devices https://creativecommons.org/licenses/by/4.0/ 2026-09-14 2026-09-14 36 3 027 037 10.51316/jst.192.ssad.2026.36.3.4 https://jst.vn/index.php/ssad/article/view/1216 The growing demand for electrical energy, together with increasingly complex operating conditions caused by nonlinear loads and the widespread integration of renewable energy sources, has led to increased harmonic distortion in power systems. As critical assets in distribution networks, transformers are particularly vulnerable to harmonic-induced losses and thermal stress, which can accelerate insulation aging and reduce service life. This study investigates the thermal effects of harmonics on a medium-voltage distribution transformer using a coupled electromagnetic–thermal approach based on the finite element method. In the proposed workflow, ANSYS Maxwell is used to compute electromagnetic quantities and harmonic-dependent losses, and these losses are then used as inputs to an equivalent electro-thermal model to predict top-oil and winding hot-spot temperatures. The electromagnetic model is validated under no-load and short-circuit conditions, and simulations are conducted for linear and nonlinear loads at 50%, 100%, and 125% of rated load. The hybrid thermal prediction results show good agreement with detailed ANSYS Mechanical simulations, with a deviation of approximately 3% in the linear-load case and 0.016% in the nonlinear-load case. The results show that harmonic loading significantly increases load-related losses and hot-spot temperature, while also revealing a non-uniform temperature distribution that enables hot-spot localization. The obtained spatial hot-spot information supports insulation loss-of-life assessment and provides practical guidance for design improvement, sensor placement, periodic inspection/testing, spare-part planning, and condition-based maintenance under harmonic operating conditions. Mr Loi Tran Van Ms Ho Nguyen Thao Mr Trung Ho Van Assoc. Prof. Bao Doan Thanh Assoc. Prof. Duong Le Dinh Dr. Engr. Anh Nguyen Kim Copyright (c) 2026 Smart Systems and Devices https://creativecommons.org/licenses/by/4.0/ 2026-05-27 2026-05-27 36 3 038 047 10.51316/jst.192.ssad.2026.36.3.5 https://jst.vn/index.php/ssad/article/view/1536 Gilgit-Baltistan lies at the periphery of Pakistan's electricity network, and in Skardu the consequences are acute. The town sits at 2,228 m, receives no piped natural gas, and endures a heating season of four to five months in which night temperatures fall between −10 and −20 °C. More than 80% of households therefore burn wood, dung cakes, crop residue, kerosene, coal, or plastic waste for warmth and cooking. This paper asks two questions: what the present fuel regime costs the people who live with it, and whether those people would pay for an alternative. We deployed low-cost optical particulate sensors indoors and outdoors through the 2023 heating season, applied a radiative forcing model of black carbon deposition to nearby glaciers, recorded household fuel expenditure, and sized an off-grid photovoltaic and storage system for the site. Measured indoor PM2.5 varied sharply by configuration: 359 to 463 µg/m3 with a wood Angeeti stove, 306 to 382 with cow dung, 261 to 375 with a kerosene heater, and 48 to 149 with a liquefied petroleum gas appliance by day, rising to 399 to 409 in that same room during the evening pollution peak. Indoor carbon dioxide reached 4,711 ppm, confirming that rooms are sealed against the cold to the point of eliminating ventilation. Outdoor daily air quality index values at the main bazaar peaked at 467. Modelled black carbon deposition adds an estimated 1 to 3 mm of water-equivalent melt per day on nearby ice. Sized on January irradiance, a 1.5 kWp array with 4 kWh of storage delivers electricity at about PKR 55 per kWh for a capital outlay near two winters of the fuel spend the highest-spending surveyed household already reports. Mr Syed Safiullah Khalid Dr. Engr Kiran Siraj Ms. Roha Rehan Assoc. Prof Naveed Arshad Copyright (c) 2026 Smart Systems and Devices https://creativecommons.org/licenses/by/4.0/ 2026-09-14 2026-09-14 36 3 048 059 10.51316/jst.192.ssad.2026.36.3.6 https://jst.vn/index.php/ssad/article/view/1466 The motion of ships and aircraft is governed by inertia and hydrodynamic or aerodynamic forces, so they do not stop immediately even when reverse thrust is applied. Consequently, operations such as maneuvering in harbors and landing require control that can decelerate and stop accurately near a target position, yet full-scale testing can be costly and constrained by safety. This study uses a small wheeled model equipped with two coaxial propellers forming an independently actuated doublefan propulsion system and examines stopping control experimentally. Position is estimated from an overhead camera by detecting a red marker on the vehicle and computing its centroid. Manual control, proportional position control, fixed reversethrust commands, and reference-velocity-based control using velocity feedback were compared. In Experiment 1, fixed reversecommand levels were varied to characterize stopping behavior. In Experiment 2, a single operator controlled the forward thruster while position-based reverse thrust was applied automatically. In Experiment 3, a reference velocity profile obtained from successful manual runs was used for velocity-based control. The results show that fixed reverse thrust can change stopping accuracy and trajectory dispersion depending on the command level, while the reference-velocity-based controller reduced the average stopping-position overshoot from approximately 9 mm to 3 mm under the tested conditions. The contribution of this study is not a new derivative-control law, but an experimental characterization of independently actuated forward/reverse fan control and the application of a reference-velocity-based stopping strategy to this specific double-fan platform. The results are limited to the tested small-scale model and are not intended as a quantitative prediction of full-scale aircraft behavior. Tetsu Miyazaki Hideki Toda Copyright (c) 2026 Smart Systems and Devices https://creativecommons.org/licenses/by-nc/4.0/ 2026-09-14 2026-09-14 36 3 060 066 10.51316/jst.192.ssad.2026.36.3.7 https://jst.vn/index.php/ssad/article/view/1489 This paper examines the influence of the hull length-to-diameter ratio (L/D) on the hydrodynamic resistance of a generic axisymmetric AUV, represented by the DARPA SUBOFF bare hull. Three-dimensional steady Reynolds–averaged Navier–Stokes (RANS) simulations were conducted in STAR-CCM+ using the shear-stress transport (SST) k–ω turbulence model. The numerical procedure was validated against the SUBOFF towing-tank data of Liu and Huang: after a grid-independence study, the predicted resistance differs from the measurements by up to 3.1% across six forward speeds (1.27% at the reference condition). A family of five hulls spanning L/D = 6.44–11.76 was then generated by affine scaling at constant displacement, and each hull was simulated at six forward speeds. At every speed the resistance, expressed through a volumetric drag coefficient, has a minimum at L/D = 8.57, with off-optimum penalties below about 2%; the coefficient decreases with increasing Reynolds number. A slight change in the relative ranking of the off-optimum hulls was observed between low and high speeds, suggesting a shift in the balance between frictional and pressure-related resistance mechanisms. The findings provide quantitative guidance for selecting the slenderness of axisymmetric AUV hulls. Danh Bui Thanh He Ngo Van Yoshiho Ikeda Copyright (c) 2026 Smart Systems and Devices https://creativecommons.org/licenses/by-nc/4.0/ 2026-08-25 2026-08-25 36 3 067 073 10.51316/jst.192.ssad.2026.36.3.8 https://jst.vn/index.php/ssad/article/view/1303 Quantum Support Vector Machines (QSVMs) have recently emerged as a promising approach for biomedical classification tasks. However, their practical deployment remains constrained by limited qubit availability and sensitivity to high-dimensional feature spaces. This study proposes a hybrid framework integrating Pearson correlation–based feature selection and Principal Component Analysis (PCA) with QSVM for breast cancer diagnosis. First, Pearson correlation analysis is employed to remove redundant and weakly relevant features. Subsequently, PCA projects the selected attributes into a compact subspace while preserving most of the original data variance. This dimensionality reduction strategy decreases the number of qubits required for quantum encoding and improves computational efficiency. Experiments conducted on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset demonstrate that the proposed hybrid QSVM achieves 98% classification accuracy, outperforming or matching existing classical and quantum-based approaches. The results confirm that combining classical preprocessing techniques with quantum classifiers provides a robust and resource-efficient solution for biomedical data analysis. Dr. Hang Dang Thuy Mr My Nguyen Van Van Tran Thi Dinh Do Van Copyright (c) 2026 Smart Systems and Devices https://creativecommons.org/licenses/by/4.0/ 2026-05-06 2026-05-06 36 3 074 080 10.51316/jst.192.ssad.2026.36.3.9 https://jst.vn/index.php/ssad/article/view/1308 The analysis of deoxyribonucleic acid (DNA) plays a crucial role in the diagnosis of genetic and DNA-related diseases such as cancer, anaemia, and cystic fibrosis. Conventional techniques, including polymerase chain reaction (PCR) combined with denaturing gradient gel electrophoresis (DGGE), provide high analytical accuracy but are often time-consuming, labour-intensive, and costly. Therefore, there is a need for simpler and more cost-effective detection strategies. In this study, we present a nanostructured electrochemical platform for DNA detection based on a highly ordered gold nanoparticle (AuNP) array. The sensing mechanism relies on electrochemical impedance spectroscopy (EIS) to monitor DNA structural changes in real time without labelling. Chemically induced denaturation of double-stranded DNA was investigated to evaluate the sensing performance. A significant impedance variation of approximately 20% was observed during the denaturation process, demonstrating the sensitivity of the AuNP-based platform to DNA conformational changes. These results confirm the feasibility of using ordered AuNP arrays combined with EIS as a simple, label-free, and cost-effective approach for monitoring DNA interactions, offering promising potential for biomedical diagnostic applications. Dr. Engr Quang Tran Anh Dr. Engr Dang Nguyen Phu Prof. Tan Tran Duc Copyright (c) 2026 Smart Systems and Devices https://creativecommons.org/licenses/by/4.0/ 2026-06-08 2026-06-08 36 3 081 088 10.51316/jst.192.ssad.2026.36.3.10