This paper is a study on a modular smart plant factory integrating intelligent solar module, LED module with high efficiency for plant growth, IoT module control system and image processing technology. The intelligent sun and modules have a corrugated structure, and the angle of the module can be adjusted to obtain a large amount of power generation. It is fully foldable for wider angles during the day and module protection at night. The LED module is designed and manufactured to distribute energy evenly over the entire wavelength range so that high efficiency can be obtained. The control system with IoT convergence technology enables control of all parts related to plant growth such as angle control of solar modules, LED lighting control, temperature/humidity control, and fan control. In particular, the control method is programmed to be controlled by a computer monitoring system and a smartphone app, so there are few places. In addition, this paper developed an image processing algorithm to extract the growth information of lettuce grown in the plant factory. The acquired images were separated into R, G, and B images using Matlab software. The applied algorithms are k-mean and improved morphological image processing. By applying this method, we can determine the area calculation and shipping of lettuce seedlings. As a result of the fusion and application of solar modules, LED modules, and IoT modules, information on plant growth and status was confirmed.

In this paper, a large-capacity white light source module using a high-power blue laser diode and a reflective spaced phosphor was designed. The reflective spacing phosphor ensured thermal stability. The proposed white light module is a reflective phosphor structure with a bi-directional optical system based on a rhombus prism lens. The rhombus prism optical system can greatly narrow the blue laser beam width and a long-wavelength band-pass filter of 500 $\mathrm{nm}$ or more is applied to change the movement path of the laser beam and transmit white light excited by the phosphor. A dichroic filter was applied to the fold mirror part and a planar convex lens was designed to focus the blue laser beam so that the phosphor was irradiated. Finally, a high-power white light source is obtained from the pre-optics unit to which the dichroic filter is applied. In order to use the proposed white light source as a searchlight, a divergence angle of 4° or less is required. For this implementation, a large-area collimating lens combining an aspheric condensing lens and an achromatic lens was applied. It was confirmed that the divergence angle of 4 degrees or less was satisfied at the focal length (FL) of 38.5 to 42.5 mm of the optical lens of the laser white light module emitter and the collimating optical system.

In this paper, nickel-plated carbon fiber was fabricated by a dry process method to improve electromagnetic wave shielding performance. In general, carbon fiber is wrapped in a polymer type in manufacturing and is used after removing the sizing in the pretreatment step for dry coating. The existing sizing removal method was used by removing only with a solution or only with compressed air. In this paper, the method of solution and compressed air (hybrid) was added. The state in which the sizing was removed was determined only by the know-how of the experienced person, and in this paper, it is proposed to represent the numerical value by applying the image processing surface analysis technique. As a result, it was possible to numerically indicate that the hybrid method was excellent among the sizing removal methods and it was possible to manufacture the nickel-plated carbon fibers (30 μm, 40 μm, and 100 μm) by a roll-to-roll sputtering method on the sizing-removed spreading carbon fiber roll. The electromagnetic wave shielding performance of 100 nickel-coated carbon fiber measured by the Korea Testing Laboratory showed the highest electromagnetic wave shielding performance from 66.7 (dB) to 73.2 (dB). This is similar to the electromagnetic wave shielding rate of copper, so it can be used as a cable for EV/HEV vehicles, and it is expected to have a great effect of improving the bending characteristics and disconnection phenomenon and improving the lifespan compared to the existing copper wire.

Most of the processes with various dynamic characteristics can be reduced to the Second Order Plus Time Delay (SOPTD) model by using the model reduction method. We propose a novel hybrid approach that combines Long Short-Term Memory (LSTM)-based real-time model identification with Genetic Algorithms to enhance the Smith predictor control structure. This method compensates for the delay time of the SOPTD model while minimizing the Integral Time Absolute Error performance index. Our approach integrates an optimally adaptive Proportional–Integral–Derivative (PID) controller design algorithm that estimates the coefficients of the SOPTD model in the Smith Predictor control structure and adjusts the PID controller parameters dynamically. The method is improved through a combination of numerical calculation, Genetic Algorithms, and LSTM networks, showing approximately 15% better performance compared to conventional methods. The system demonstrates significant improvements in both performance metrics and resource utilization, including a 40% reduction in execution time and enhanced resource efficiency. Simulation results show that the proposed scheme exhibits improved adaptability to disturbances and process variations, with faster response times and reduced overshoots compared to traditional methods. The steady-state response of the higher-order model and the reduced model shows perfect matching for the unit feedback input.

In this paper, the electrical submersible pump (ESP) is combined with information and communication technologies (ICT) to diagnose the operation status and soundness of the ESP. The ICT convergence provides users with maintenance and repair information through history management and remote control in case of failure. The proposed system includes a monitoring unit (MU) that senses the operating state of the ESP, a monitoring control unit (MCU) that transmits the sensed information to internal and external databases, and a monitoring system that allows users to check the status information. The server and embedded system can monitor the operation status of the submersible pump by storing sensor values in the database and displaying them on the screen. The embedded system retrieves the sensor values stored in the database and performs individual and complex diagnoses. The diagnosis results are sent to the server and status information to the monitoring control unit. The diagnosis of the submersible pump was divided into 23 individual sensor classifications, and a multilayer perceptron was implemented. Weights were set through learning and testing. The proposed ICT-converged Smart ESP is considered to be competitive as it greatly improves the existing system in terms of status and maintenance.

This paper proposes a hybrid time–frequency domain identification method for second-order plus dead time models with an additional process zero (SOPDT+Z). A dual-relay experiment combined with step response data provides six independent equations for five model parameters, whose mathematical well-posedness is established through Jacobian rank analysis. A cascaded initialization strategy (Sundaresan → SIMC → Jin → proposed) guarantees monotonically improving accuracy. An Internal Model Control (IMC) framework yields equivalent PID parameters with a single tuning parameter λ, supported by a formal robust stability theorem. Simulation studies on five benchmark systems demonstrate 60–100% reduction in open-loop IAE compared to existing SOPDT methods, 36% faster settling, and 100% closed-loop stability under ±20% Monte Carlo perturbation (N = 200). Noise robustness analysis under SNR = 20–40 dB and additional performance metrics (ITAE, ISE) further validate the method.