Journal of Tomography System and Sensor Application

Finite Element Analysis and Design Optimization of Heavy-Duty Storage Systems for Automated Palm Oil Harvesting

Wed, 01 Jul 2026 00:00:00 +0000

The palm-oil industry remains heavily dependent on manual labour for harvesting fresh fruit bunches (FFB), which creates challenges in productivity, cost, and worker safety. To support the move toward automation, this study focused on the design and validation of a square-shaped storage unit for an automated palm-oil harvester. A 3.0 m³ detachable storage bin with a maximum rated capacity of 900 kg was specified, reinforced with a steel frame and mesh walls. Analytical flexural strength checks were carried out on two representative faces of the bin, and finite element analysis (FEA) was performed in Autodesk Inventor to assess structural performance under a worst-case load of 10.4 kN. The results indicated that the maximum von Mises stress was 35 MPa, which remained below the allowable stress of 45.7 MPa, while the maximum displacement was limited to 1.695 mm at the centre of the panel. The safety factor analysis confirmed a minimum value of 15, matching the design target. These findings validate the structural adequacy and reliability of the proposed storage design and highlight its potential to reduce labour dependency in palm-oil harvesting.

Electrical Capacitance Tomography Simulation for Palm Oil Clarifier Applications

Rohani Arpa, Nur Tantiyani Ali Othman — Wed, 01 Jul 2026 00:00:00 +0000

Electrical capacitance tomography (ECT) is a low-cost and non-invasive technique for imaging multiphase flows. However, its application in palm oil clarification is limited by rectangular tank geometry, internal heating structures, and temperature-dependent permittivity variations. This study develops a multiphysics simulation framework in COMSOL Multiphysics® (v6.2) to evaluate ECT performance in a 20 m × 10 m clarifier equipped with four vertical heating rods and twelve external electrodes. Coupled laminar flow, transient heat transfer, and electrostatics simulations capture buoyancy-driven circulation, where the heating rods generate vigorous central mixing and peripheral low-velocity zones. Mesh independence analysis confirms that the fine mesh accurately resolves thermal and flow gradients with stable convergence. Electric potential simulations at 1, 5, and 10 V indicate that 5 V effectively balances field penetration and stability, while central electrodes yield more symmetric field coverage. These findings offer practical guidelines for electrode placement and excitation strategy, supporting the implementation of ECT as a real-time monitoring tool in palm oil processing.

Development of A Walker for Fall Prevention

Wed, 01 Jul 2026 00:00:00 +0000

This study explores the design and development of a simple, easily deployable walker aimed at preventing falls among adults and the elderly. They face challenges in regaining their ability to walk after experiencing a traumatic illness, an accident, a prolonged period of sickness, or simply being old and weak. This situation can create a vicious cycle: as their fear of falling increases and their physical activity decreases, they suffer an even greater loss of walking ability. Consequently, their quality of life worsens. The design is intended to support a body weight of 120 kg. In principle, the walker can support an adult without tipping over as long as the body's center of mass remains within the base of support. The walker has been designed, and its structural strength is simulated in SolidWorks. A customized inertial-measurement-unit (IMU) system is intended to be worn by the adult for the purposes of detecting falls, alerting users, and data acquisition. This paper demonstrates the design principles and procedure for a walker for fall prevention. The results indicated that the design could be revised repeatedly to meet the specified targets, and the trials confirmed the expected outcomes. Future enhancements could include additional straps to secure the body to the walker during a fall and the integration of IoT technology for remote alerts.

Ultrasonic-Based Wall Thickness Measurement and Structural Defect Detection: An Initial Study

Rahma Husein Mohamad Abdeldayem, Herman Wahid — Wed, 01 Jul 2026 00:00:00 +0000

Structural health monitoring is important to ensure the safety and durability of buildings and infrastructures. Traditional inspection methods are often expensive and may not detect hidden defects. This paper presents a low-cost ultrasonic-based non-destructive testing (NDT) system designed for wall thickness measurement and structural fault detection. The system uses a 300 kHz ultrasonic transducer with an STM32 microcontroller and Raspberry Pi for signal processing. A transmitter generates short ultrasonic pulses, and the receiver collects echoes from surfaces and defects inside the material. Signal conditioning circuits, including amplification and filtering, improve the detection of weak echoes, while digital processing is used to calculate wall thickness and identify cracks or corrosion. Tests show that the system can measure variations in thickness and detect different types of faults effectively. The proposed design offers a portable and affordable tool for monitoring structural integrity, which can be applied in civil engineering and preventive maintenance.

Classification of beef and pork meat by using SVM algorithm

Imam Syaukani, Siti Zarina Mohd Muji, Suhaila Sari, Muhammad Sazlan Abdul Kadar — Wed, 01 Jul 2026 00:00:00 +0000

Meat is the main parameter in human daily consumption, because the excess protein in meat can increase human intelligence and stamina, but the similarity in color and texture of two types of meat makes it difficult for the human eye to distinguish them. This is used as a loophole by irresponsible meat traders to mix two types of meat, so it can cause serious health conditions in humans. Meat can generally be distinguished by looking at the fibers and colors of certain meats. Therefore, this study will overcome this problem by prioritizing the parameters of different colors and textures of meat which are classified as optimal methods with digital image processing. Among the efficient approaches offered is to apply the support vector machine (SVM) method by extracting HSV features in distinguishing beef and pork categories. This study adopts SVM and HSV feature extraction in finding out the types of beef and pork with fairly good algorithm results of 97% in 510 images, which were divided into training and testing data. The dataset used in the study is taken from Kaggle as an open-source platform for any practitioner. As a result, this technology shows a very accurate opportunity in detecting the type of meat correctly.

Simulation of Ultrasonic Response to Early Corrosion in Reinforced Concrete

Wed, 01 Jul 2026 00:00:00 +0000

Corrosion of steel reinforcement is a major cause of deterioration in reinforced concrete (RC) structures. It often starts at the steel–concrete interface and progresses silently, without any visible signs of damage, until significant deterioration has occurred. Early detection is therefore crucial to enable timely maintenance and extend service life. This study investigates the feasibility of using ultrasonic techniques to detect early-stage corrosion in RC through finite element (FE) simulations. A two-dimensional model was developed in COMSOL Multiphysics, with a steel rebar embedded within a concrete block. Early corrosion was represented as a thin annular layer with reduced stiffness and density surrounding the steel bar, and three cases were simulated: intact steel, 1 mm corrosion layer, and 2 mm corrosion layer. The simulation results showed that thicker corrosion led to reductions in wave amplitude, delays in arrival time, and distortion of the waveform, which indicates changes in acoustic impedance and scattering at the degraded interface. These findings demonstrate the potential of ultrasonic-based methods for detecting corrosion at its earliest stages and provide a foundation for developing more sensitive structural health monitoring techniques for RC infrastructure.

Exploring Alternative Current Injection Patterns in ERT to Enhance Central Sensitivity: A Multi-Injection Measurement Strategy

Wed, 01 Jul 2026 00:00:00 +0000

Electrical Resistance Tomography (ERT) is a promising imaging technique for industrial applications, yet it suffers from low sensitivity in the central region of the imaging domain, limiting its effectiveness in detecting internal anomalies. To address this challenge, a novel multi-injection strategy is proposed and investigated in this study. Simulation results demonstrate that the new strategy enhances detection capability in the central region compared to conventional approaches. However, the reconstructed images show minimal improvement over the conducting boundary method due to the inherent limitations of the Linear Back Projection (LBP) algorithm, which yields low-resolution outputs. These findings highlight the potential of advanced injection strategies while underscoring the need for improved reconstruction algorithms to fully realize their benefits.

A Review of Non-Invasive Ultrasonic Therapeutic for Liver Cancer Elimination

Wed, 01 Jul 2026 00:00:00 +0000

Liver cancer, a leading cause of cancer-related deaths globally, poses significant treatment challenges due to its late-stage diagnosis and complex treatment options. Non-invasive therapeutic techniques have gained attention as promising alternatives for cancer treatment, with ultrasonic therapy being one of the most innovative approaches. This study explores the potential of non-invasive ultrasonic therapeutic methods for the elimination of liver cancer cells. By utilizing high-frequency sound waves, the therapy can target and disrupt cancerous tissues with minimal damage to surrounding healthy structures. The effectiveness of ultrasonic waves in penetrating liver tissues, coupled with their ability to induce thermal effects and cavitation, makes this approach suitable for focused treatment of localized tumors. Current technologies for liver cancer treatment include surgical resection, liver transplantation, radiofrequency ablation (RFA), trans arterial chemoembolization (TACE), and systemic therapies such as targeted drugs and immunotherapy. However, these methods have limitations, including invasiveness, high recurrence rates, systemic side effects, and limited effectiveness in advanced-stage cases. Preliminary results from clinical trials indicate that non-invasive ultrasonic therapy can induce tumor regression and enhance liver function without the need for surgery or chemotherapy. This report reviews the mechanisms of action, the technological advancements in ultrasonic equipment, and the clinical outcomes to date, demonstrating the potential of this non-invasive technique as a viable therapeutic option for liver cancer treatment. Further research is needed to optimize treatment protocols and evaluate long-term efficacy and safety.

A Machine Learning Approach for River Pollution Assessment from Camera Images

Wed, 01 Jul 2026 00:00:00 +0000

This study focuses on developing a machine learning system for camera-based river pollution assessment using image analysis and Convolutional Neural Network (CNN) architectures. The objective was to design a predictive model, evaluate different CNN architectures, and utilize camera-captured images to predict river pollution. By integrating colour analysis, reflection analysis, turbidity analysis, and object recognition with CNN-based image analysis, a comprehensive and automated method for evaluating river pollution was developed. The study also evaluated several CNN architectures, including MobileNet, CNN Sequential, and VGG16, to identify the most effective model for river health assessment. The results showed that the VGG16 architecture consistently achieved the highest accuracy, thanks to its deep layers and intricate structure that allowed for precise pattern recognition and analysis. The developed machine learning system, integrated with VGG16 and incorporating multiple image analysis techniques, provides a robust and accurate solution for predicting and assessing the pollution of rivers based on camera images. This comprehensive approach enables proactive measures and timely interventions to protect and improve overall river health.

Automatic Load Control and Energy Optimization in Smart Building Using PLC and SCADA

Sophan Wahyudi Nawawi, Allson Shauyie Say, Sulaiman Sabikan — Wed, 01 Jul 2026 00:00:00 +0000

The proposed research project seeks to develop an Automatic Load Control and Energy Optimization system of smart buildings based on the principles of SoftPLC technology and Virtual Commissioning. The system is aimed at recreating industrial automation settings to efficiently control energy use without the limitation of physical device. It has a Web-based SCADA Dashboard (WebVisu) that helps to monitor in real time energy metrics, virtual sensor input, and load status, and it can be viewed using any standard web browser. It requires the backend infrastructure to enable the logging of data (high-frequency data) and historical analysis through the integration of a local MySQL database deployed in XAMPP. This paper will deal with the shortcomings of the already existing passive building management systems by proposing a flexible software-based architecture that has the capability to make intelligent decisions. The switch to the hardware-dependent control to a Digital Twin type of simulation is done through the use of CODESYS Control Win V3, the Modbus TCP/IP communication control, and the Node-RED middleware. The initial testing shows that the software layers have indeed been integrated successfully, which proves that the communication backbone is stable and that the data logging pipeline is correct. Future suggestion also involves application of Fuzzy Logic Control (FLC) and Stochastic Occupancy Profiling to increase more on the flexibility of the system. This thorough research does not just confirm the practicability of Virtual Commissioning to build automation, but it also provides a strong ground on the formation of smart, data driven energy management measures.