Self-learning enables students to learn at their own pace. The process itself has changed significantly from reading textbooks to viewing ebooks on electronic devices, asking questions on online discussion boards or watching video clips from other parts of the world. The change has come with a massive increase in the amount of information available. It is time-consuming for a student to obtain the exact item of class material wanted. We developed a search system that can store relevant class materials in its own database and give suggestions when a student needs them. The system is built as a chatbot in the LINE application, using the NLP algorithm, to interact between users and the system via Artificial Intelligence technology. The system was developed for two subjects in Electrical Engineering: Signals and Systems and Control Systems. It was able to correctly interpret the users’ requests with up to 80% accuracy. It can give suggestions of class materials based on rankings given by users’ scores. This search system makes self-learning less time consuming and may provide more encouragement to students.
The output from a digital Micro-Electrical- Mechanical System (MEMS) microphone in the form of PDM often needs to be converted to PCM before further processing, as PCM signals are easier to analyze. The current hardware-based PDM-to-PCM converters uses cascaded integrator-comb (CIC) based and finite impulse response (FIR) filters, which result in high hardware utilization to achieve a high signal-to-noise ratio (SNR). To strike a balance between performance and power consumption, a one-dimensional convolutional neural network (1D-CNN) has been applied in a PDM-to-PCM converter. Although this method resolves the aforementioned issues, an improvement to the system latency and throughput is possible. This paper proposes a fast method for a hardware-based PDM-to-PCM converter by cascading a digital low-pass filter and an existing ID-CNN-based low-pass filter. The approximation results show that the output PCM signal has the mean absolute error (MAE) of only 0.0026 compared to the original PCM signal. The proposed method has been implemented on the Xilinx PYNQ-ZI field programmable gate array (FPGA). While there is a slight increase in hardware utilization due to an additional required hardware, the latency has improved by 61% compared to the existing ID-CNN-based PDM-to-PCM converter. This research reduces the time taken to process each PCM data from PDM in a hardware-based system.
This research examines the quantum Hall effect of a GaAs/AlGaAs heterostructure sample at the temperature of 4.2 K and measures the longitudinal R xx and Hall resistance R H at $I = 10 \mu \mathrm{A}$. The corresponding Hall plateaus are represented by the depicted longitudinal R xx and Hall resistance R H curves, however, the Hall resistance R H ’s characteristics are not complete. The odd number’s plateau index i was not observed. At $i = 2$ and $i = 4$, the longitudinal resistance characteristic R xx approaches zero. Comparing the measurement results at 1.5 K (theoretical) and 4.2 K, it was discovered that there were deviations of $1.5 \mathrm{m}\Omega / \Omega$ ($i = 2$) and $5 \mathrm{m}\Omega / \Omega (i =4)$, respectively. The effect of rising temperature is a factor contributing to the quantum Hall effect’s incompleteness. We can identify abnormalities while measuring or determining the time period of operation from the characteristics of the measured results. Moreover, the external magnetic field is measured around the Quantum Hall Resistance (QHR) standard system. It will be able to securely evaluate the effects of magnetic field generation produced by the superconducting magnet in the cryostat system. From the results, there is a small external magnetic field around the QHR system.
Gamification has been widely used in recent years to enhance the learning experience of various subjects, including programming. However, there is a lack of clear and structured approach to implement gamification in programming education. This aim of this research is to develope a set of steps for efficiently learning programming through a game. The proposed steps cover the essential aspects of learning programming such as problem-solving, debugging, and understanding of programming concepts. The study also found that the proposed steps are flexible and also be applied to different programming languages and level of learners. The proposed steps also can be used as a guide for educators and trainers to implement the proposed step in programming education to make the learning process more engaging, interactive and fun. The study concludes the proposed step can be a valuable resources for educators and trainers to use gamification in education.
This paper proposes a performance analysis of using a static volt-amp-reactive (VAR) compensator for a three-phase self-excited induction generator (SEIG) operating as a single-phase induction generator. The study proposes appropriate capacitor for building up voltage supplying power to load with static VAR for regulating terminal voltage. The simulation model is analyzed by using MATLAB/Simulink under various conditions such as reactive power supplied, dynamic response, steady state, building up voltage for startup, linear load, non-linear load and harmonics in system. From the study (based on the study, the findings revealed it is found) that using the static VAR compensator connected in parallel with loads for supplying reactive power to the load systems is needed for maintaining terminal voltage.
A Near-field (NF) extrapolation study for cylindrical near-field to far-field transformations is presented in this paper. The NF extrapolation techniques are studied in this work including the linear technique, nearest data technique, and piecewise cubic Hermite interpolating polynomial (PCHIP) technique, in order to reduce the effect of NF truncation data on FF antenna radiation pattern error. The cylindrical wave expansion with trapezoidal rule numerical integration is employed in the NF-FF transformation algorithm. In the study, NF data of the seven-patch array antenna is simulated by the commercial simulation program, where the NF sampling length of the cylindrical scan of 1.6 meters is employed. The NF extrapolation is applied to increase the NF sampling length of the cylindrical scan from 1.6 meters to 2.4 meters. It is found that the far side-lobe accuracy of AUT FF radiation patterns from the proposed technique using linear extrapolation is improved. The far side-lobe FF antenna radiation pattern error is reduced. The truncated scanning length of NF data provides less time-consuming NF data collection. The FF antenna radiation pattern error will be reduced by using appropriate NF extrapolation techniques.
Fingerprint-based indoor positioning systems are simple and widely used to determine the location of a device inside a building or other enclosed area. However, the accuracy and reliability are still a major concern due to the turbulence in the environment and the presence of noise in the data. This paper presents a machine learning integrated with Kalman filter approach for improving the accuracy of fingerprint-based indoor positioning systems. The proposed approach combines the power of machine learning techniques for feature extraction and classification with the noise-filtering capabilities of the Kalman filter. Implementation is achieved by a real-world dataset collected from multiple Bluetooth low energy access points. The experiment results indicate that the proposed approach significantly improves the accuracy of fingerprint-based indoor positioning compared to traditional machine learning approaches. This study also offers a potential of cost-effective and high accuracy algorithm for the indoor positioning applications.
This study aimed to create a mobile application to promote and guide the cultural tourism attraction Phra That Nine Choms in Chiang Rai Province, Thailand. The critical information about Phra That Nine Choms is gathered and analyzed to identify the problem and user requirements. The operation of the mobile application is designed using a use case diagram, class diagram, and sequence diagram. The Java programming language creates the mobile application through the Eclipse program, Android SDK, and JAVA JDK development tools. 95 Thai and foreign tourists tested the resulting application. Their satisfaction was evaluated through a questionnaire and analyzed. The interpreting results showed that the mobile application is suitable for providing travel recommendations and can serve as a guide for future development to support tourism activities, particularly for cultural and local attractions.
This paper proposes the development of an RFID envelope coated with an antimicrobial material that can stop microorganisms’ growth while the card is still regularly used. Copper is chosen as the antimicrobial material for coating on an envelope made of polyethylene terephthalate (PET) by using DC sputtering. The antimicrobial ability of the proposed envelope is investigated against Escherichia coli (E. coli). The result shows that the survival time of E. coli on the proposed envelope is decreased compared with the PET envelope without the copper thin film coated on. Besides its antimicrobial quality, copper can let radio frequency pass through. Thus, the effectiveness of electromagnetic wave transmission through the proposed envelope coated with different thickness of a copper thin film is tested. To evaluate the performance of the proposed envelope, three experiments which cover the test of the antimicrobial ability of copper thin film, the resistance of copper thin film, and performance of RF transmission of RFID reader and tag are conducted. The results of the three experiments show that a copper thin film can prevent the microorganisms’ growth, the resistance of the copper thin film is decreasing while the thickness of the copper thin film is increasing, and the radio frequency can pass though the proposed envelope.
The generation of electricity is transitioning away from fossil fuels and toward renewable energy. Power electronics dominated power systems, resulting in low-inertia grids. Virtual inertia control strategies have been developed to compensate for lacking inertia. The conventional controller, however, is not adjustable to diverse system dynamics due to the fixed gain of inertia. A fuzzy-based adaptive virtual inertia controller is proposed in this study by focusing on grid frequency responses and energy consumption of energy storage. The controller is tested using a major disturbance, such as a big load shift. The results reveal that an adaptive gain of inertia help improves frequency response while consuming less energy than a fixed inertia gain.
In this paper, we use transfer learning technique to train a custom Thai Buddha amulet coin classifier. The trained model is deployed on a server allowing a dedicated mobile application to request for class prediction results. The trained model achieves good accuracy using relatively small training image dataset. It is important to note that the primary objective of this work is not to evaluate the authenticity and quality of an amulet, but rather to create a prototype system that can be used as a tool to classify types of amulets.
A multiband tri-branch monopole antenna is presented. The antenna structure is constructed of radiating patches with three branches. It is fed by a microstrip transmission line. Essentially, the antenna is a simple structure created by a step impedance technique. In addition, the antenna can be separately regulated for each operational frequency band of 2.44 GHz, 3.36 GHz, and 5.52 GHz by the height of a central patch, a right patch with a thin strip line, and a small patch on the left, respectively. The antenna is manufactured on a size of 50x35 mm 2 FR4 substrate with a thickness of 0.8 mm and a permittivity of 4.3. The suggested antenna is appropriate for network board internet of things (NB-IoT) technology working at 2.4 GHz/5.2 GHz/5.8 GHz (WLAN), 3.5 GHz (WiMAX), and 2.3 GHz/2.6 GHz/3.5 GHz (5G technology). The antenna's radiation pattern is omnidirectional, and its average gain is approximately 2dBi.
The single-phase grid-connected photovoltaic (PV) system was studied by simulating in MATLAB-Simulink program. This research aimed to study the influence of two parameters on the maximum power point tracking (MPPT) efficiency, e.g., a PV capacitor (C PV ) and voltage step size. The maximum power point was tracked by using the perturbation and observation (P&O) algorithm with the PV voltage-loop and current-loop controller of the converter. The system was able to simulate a sudden increase or decline in irradiance. The result showed that the reducing voltage step size can track for better maximum power point of the PV array. The ripple voltage and ripple power have reduced values. The increased C PV value affects the overshoot, which has a reduced value and a faster response time for returning to a steady state. Therefore, the suitable C PV of 200 μF obtains the MPPT efficiency of 99.969% with a reduced overshoot and faster time response.
This research proposes passive techniques for magnetic field mitigation in the 69/115 kV underground power system installed by the Metropolitan Electricity Authority (MEA). The passive techniques which are: conductive loop, conductive shield, and ferromagnetic shield were designed and simulated by using the Finite Element Method Magnetics (FEMM) program. A comparison of the magnetic field with and without a mitigation system is obtained. Finally, the simulation results show that all passive techniques can reduce the magnetic field values to be lower than the reference value given by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and European standards.
Corrosion is one of the biggest problems that can lead to fatal disasters in the industry. Investigate corrosion and perform timely maintenance on the asset to prevent corrosion issues. However, the investigation of the inspector onsite can lead to a time-consuming and dangerous problem. For that reason, corrosion detection from offshore asset images is necessary. This paper proposes the implementation of a segmentation technique for automatically detecting corrosion damage on oil and gas offshore critical assets. We compare three semantic segmentation architectures, namely UNET, PSPNet, and vision transformer. The image data was collected by unmanned aerial vehicles (UAV). The experiment also compared the full-image dataset and sliced-image dataset with 512 × 512 pixels of the image. The results are calculated using the F1 score and IoU score of the predicted and annotated mask. The experiment shows that ViT-Adapter trained with a full-image dataset receives the best IoU score and F1 score, which are 0.8964 and 0.9451, respectively. However, the specialist inspector prefers the result from the slicing experiment since the slicing prediction offers a more precise corrosion mask.
This paper presents to use an orthogonal property of code for identifying base station and users in a cellular network. In addition, the principle of semi-blinds is applied with LS algorithm to estimate fading channel and reduce the effect of pilot contamination. The mixing parameter combines the coefficients of weight vector obtained from the pilot signal and the data. The simulation results confirm that the proposed technique can improve performance achievable downlink rate higher than that of both the LS with code for identifying base station and conventional LS algorithms.
This paper presents a data cryptography system based on chaotic systems (Lorenz and jerk). The system implementation consists of Field Programmable Analog Array (FPAA) and a microcontroller for transmit and receiver sides. MATLAB/Simulink provides the simulation results in various parameters. The experimental setup is exhibited by applying the data of digital pictures, which are encrypted by the selected chaos model and transmitted by using the serial port. The receiver can be correctly decrypted if and only if the chosen chaos model and parameters are perfectly matched with the transmitter. The experimental results of BER were measured around $42.39\times 10^{-6}$ for the Lorenz system and $38.29\times 10^{-6}$ for the jerk system with a time of approximately 24 minutes to recover all 10,002,432 bits
Train position and velocity determination are of great importance for railway systems since uninterrupted data of train position and velocity is required for the signaling system to manage the traffic and maintain the safety of railway network. Hence specific train positioning system using a satellite network is developed. Due to very high investment, the satellite train positioning system is available in only some countries and limited classes of vehicles. Train localization using GPS data is an alternative, and a dead reckoning system is integrated to handle intermittent GPS data. Since the dynamic model of the train is unavailable, the proposed estimator is created by using a PID controller and double integrator. The developed system is installed and tested on a commuter train in Bangkok and the device can provide the train position and velocity in real-time without interruption.
Plant factory artificial light (PFAL) is a suitable method for growing high-quality plants and increasing crop production to a very high number per area. The major goals of this paper are to implement a NB-IoT based semi-PFAL growing system that use two types of LED array, there are RBLED and phosphor converted LED (pc-LED). Then compare the Cos-lettuce yields under various LED light sources while studying the characteristics of the artificial light spectrum. The semi-PFAL growing system, was controlled for watering, lighting and monitored system operating, temperature and humidity by a NB-IoT module with MAGELLAN platform. The results show that the Cos-lettuce grown under pc-LEDs are probably more capable of photosynthesis than those using RBLEDs. The average fresh weight of the Cos-lettuce from pc-LED significant higher than RB-LED at p<$\theta.\theta$5. The measurement results of environmental parameters, control of lighting and watering in semi-PFAL system, data collected from cloud system under MAGELLAN platform during 20-day of experiment, it was work accurately. The semi-PFAL growing system could be utilized to produce organic vegetables in a home or school setting.
This paper describes the design of a low-power temperature sensor in a 0.18-μm CMOS technology. The proposed temperature sensor employs the so-called “dynamic threshold MOS (DTMOS)" diode-connected transistors as the temperature sensing devices. Process spread of the MOSFET threshold voltage is compensated by using the 2-transistor (2T) voltage reference to generate the bias current sources. A charge-balancing delta-sigma $(\Delta \Sigma)$ analog-to-digital converter (ADC) is used to obtain the digital representation of temperature values. The DTMOS temperature sensor core and the ADC operate with 1 V power supply voltages. The ADC operates with a 64- kHz clock frequency and each temperature conversion time is 32ms. After a single-point temperature trimming and a linear fit, the proposed circuit achieves a maximum inaccuracy of ±0.25°C (3σ) across all process corners and the temperature range of −20°C to 85°C, while consuming 8.1 μW.
