Engineering Access

A New Scenario Management Model in the Establishment of Administrative Measures

2024-03-05T09:34:24+07:00

This research aimed to study the magnitude of the impact resulting from changes in the factors of the future scenario policy under Thailand's sustainability policy for the upcoming two decades, spanning from 2020 to 2039 by developing a best linear unbiased estimator (BLUE) model. The model was named structural equation modeling based on autoregressive integrated moving average with observed variables at first difference (SEM-var(1)). The central gap between this model and other model was the applicability of the proposed model for effective medium-term forecasting without spuriousity. As for the findings, the latent variables of the three sectors were causally found upon using the SEM-var(1) model with the highest performance, with a mean absolute percentage error (MAPE) of 1.50 percent and a root mean square error (RMSE) of 1.75 percent. Furthermore, the new scenario policy was established by requiring at least 20 percent green technology and keeping future total energy consumption (2020-2029) below the country's carrying capacity. As a result, the future CO₂ emission growth rate (2020-2029) would be 7.05 percent (2020-2029) or 39.01 Mt CO₂Eq (from 2020-2029), which was less than the carrying capacity set by not exceeding 65.04 Mt CO₂Eq (from 2020-2029). As for the administrative measures, Thailand must operate administrative legislation measures within environmental law by considering three key elements: 1) Principles of Environmental Protection Measures Planning, 2) Principles of Environment Damage Management, and 3) Principle of Polluters Pays. This result further indicated that the model is best suited for application in the formulation of future national administration plans.

Integration of Object Detection in Crime Scene Investigation

2024-05-15T09:24:29+07:00

Object detection is applicable in various fields, encompassing Crime Scene Investigation. The act of capturing evidence through photography in crime scenes is of paramount importance. Concurrently, object detection and scene analysis can be integrated by the investigator during this process. Nonetheless, the investigative procedure involves multiple phases. In this research, object detection is employed to identify crucial evidence discovered at crime scenes, along with objects commonly linked to assault cases as statistics reported by the National Institute of Justice (NIJ) in the United States of America. Challenges such as pen guns and illegal firearms persist in criminal activities, although they are not no category of legal firearms. Transfer learning techniques are adopted in this study from an existing model, utilizing pre-trained models to identify crucial evidence in crime scenes. The experiment gathered 494 images of pen guns, illegal firearms, and associated objects from online police news reports to form a custom dataset. The process of annotating, training, evaluating, and fine-tuning the custom dataset led to experimental outcomes demonstrating a high Mean Average Precision (mAP) across all target’s custom datasets. The model reached convergence at approximately epoch 100, achieving high precision as 0.97, recall as 0.926 and box mAP50 of as 0.974 Additionally, box mAP50-95 as 0.817 However, this paper presents a confusion matrix of customized specific classes with a low volume dataset. These findings highlight the potential application of object detection in crime scene investigations with the aid of object detection. Consequently, this approach could aid in formulating a project blueprint for a Crime Scene Investigation model and furthering the detection of evidence objects in the future with very large volume of dataset. In conclusion, this study illustrates the capability of detecting pen guns, illegal firearms, and related objects through object detection techniques.

Enhancing 5G Data Transmission Through Sub-Carrier Spacing Optimization

2024-04-24T22:22:56+07:00

5G networks can support various UDP and TCP applications. Efficient and reliable 5G network performance offers faster speeds, improved connectivity, and the capability to manage multiple applications across different locations. This study comprehensively analyses the effect of various subcarrier spacing (SCS) scenarios of 5G networks on the performance of smartphones, cameras, and sensors. The analysis shows that higher SCS values are associated with increased average throughput. It indicates that higher subcarrier spacing values enable more efficient data transmission. Smartphones have low and symmetrical jitter with numerology values of 3 and 2, making them well-suited for bidirectional communication. In comparison, cameras are more efficient than sensors at delivering data with a lower delay at all SCS values, which is crucial for applications requiring fast response times. We propose an adaptive Q-learning-based algorithm that automatically adjusts (SCS) configurations based on real-time network conditions and application needs. This approach significantly enhances network performance across various scenarios. The findings of this study have significant implications for the design and implementation of 5 G networks, as they provide insights into the optimal SCS settings for applications with specific requirements and priorities, thereby guiding network engineers and professionals in their decision-making process.

Design of a High Gain Low Noise Amplifier at 3.3 GHz using CMOS Technology

2024-05-07T10:45:23+07:00

Low noise amplifier (LNA) is the important device used in field of communication. The main objective of this device is to boost the level of low power signal to a sufficient level without altering the signal to noise ratio in the circuit. This paper proposes a novel LNA with improved gain and optimized noise figure using Gain Enhancement and Image Rejection (GEIR) technique. CMOS technology is used in proposed work. LNA using CMOS at 90 nm technology is proposed using image rejection. The proposed amplifier boosts signal amplification using a 4 transistor CMOS GEIR network by providing proper input and output impedance matching. Simulations are performed using Cadence tool. The proposed amplifier is operating at a center frequency of 3.3 GHz and is able to achieve 14.5 dB gain and low noise figure. This could be useful in WIMAX application where high speed data rate at wide area coverage is required.

The Predicting The Optimal Compressive Strength of Sustainable Brick Using Response Surface Method (RSM)

2024-05-07T10:49:52+07:00

This research uses the Responses Surface Method (RSM) approach to make and optimize environmentally friendly bricks using a PET and epoxy resin mixture. The main input factors for the mixture are PET particles and epoxy resin as adhesive materials, while compressive strength is the primary response of the sustainable brick produced. RSM-based Central Composite Design (CCD) was used to assess the influence of PET particle variables (1-5mm) and epoxy resin ratio (30-50%) on the compressive strength response. The accuracy of the mathematical model created by CCD was tested using ANOVA. The RSM evaluation results show that the empirical findings suit linear and quadratic models for cost response and compressive strength. A coefficient of determination greater than 0.85 for all reactions indicates that the model can explain response variability. The optimization results show that the input variables PET particle and epoxy resin ratio have an average optimum value of 40% epoxy resin, each with a PET size of 3 mm. This optimal combination produces a maximum compressive strength of 13.37 MPa. The study and application of a mixture of PET particles and epoxy resin in making sustainable bricks has shown that these materials have great potential to increase the compressive strength of sustainable bricks.

Carbon Capture Based on Chemical Absorption: Process Design and Techno-Economic Assessments

2024-06-24T15:09:46+07:00

It is a widely accepted scientific fact that emissions of greenhouse gases, mostly Carbon dioxide (CO2) from fossil fuels, contribute to global warming. However, the world's energy industry continues to rely mostly on fossil fuels, which still provide an 85percent of the world's energy needs. The realization has set in that fossil fuels would remain the main energy source for many years due to the lack of economically viable sources of renewable power and the availability of cheap fuels including coal. Consequently, it is imperative to create technology that allows for the continued use of fossil fuels while reducing the amount of Carbon dioxide released into the environment. To minimize emissions into the atmosphere, CO₂ from pollution sources should be captured. The theory behind several methods of CO₂collection will be examined in this study, and their benefits and drawbacks will be considered. After that, a selected separation method will be thoroughly examined by running simulations of the process utilizing the program As-pen Plus with three solvents, including NH3, DEA, and MEA. The effectiveness of the separation process was examined concerning operational circumstances. In contrast with other solvents, DEA stands out because of its increased CO2 removal efficiency and its decreased sensitivity to lean loading.

Enhancing Lung Cancer Diagnosis through CT Scan Image Analysis using Mask-EffNet

2024-05-15T10:30:53+07:00

CT scans efficiently detect lung cancer. A good prediction method is crucial. Recently, deep convolutional neural networks (CNN) have influenced picture categorization algorithms. This article presents a hybrid strategy using an upgraded deep transfer learning EfficientNet and a masked autoencoder for image-based distribution estimation (MADE). MADE improves feature acquisition, dimensionality, uncertainty, imbalanced data, transfer learning, and model interpretability before lung cancer categorization. These benefits improve classification accuracy and data use. Mask-EffNet, the proposed model, has two phases. The initial phase uses MADE to extract features. Using a pre-trained EfficientNet model, types are classified next. Mask-EffNet is tested using EfficientNetB7. The study uses the "IQ-OTH/NCCD" benchmark dataset, which includes lung cancer patients classified as benign, malignant, or normal. Mask-EffNet has 98.98% test set accuracy with ROC scores of 0.9782–0.9872. We tested the suggested pre-trained Mask-EffNet against different CNN architectures. The EfficientNetB7-based Mask-EffNet outperforms various CNNs in accuracy and efficacy, as expected.

A Hybrid Method based on BWM and TOPSIS-LP Model to Assess Computer Numerical Control Machines

2024-07-04T15:26:30+07:00

The escalating global competitiveness within the industrial sector has necessitated a critical requirement to enhance facilities to meet market demands. In this context, the selection of computer numerical control (CNC) machines plays a vital role in enhancing productivity and manufacturing flexibility. This study presents a hybrid method combining the Best-Worst Method (BWM) with a novel TOPSIS linear programming (TOPSIS-LP) model for CNC machine selection. The BWM is employed to address information challenges and simplify data collection by assigning weights to specified criteria. These weights are then utilized in the TOPSIS-LP model to rank alternatives based on their closeness coefficients. A numerical illustration is conducted to validate the proposed model, with outcomes compared to established Multi-Attribute Decision-Making (MADM) methodologies. The Spearman correlation matrix reveals strong correlations between the proposed method and existing methods, such as MOORA (0.96) and WASPAS (0.88), highlighting the hybrid approach's reliability. This method provides a straightforward and reliable tool for organizations to make informed CNC machine selections, ultimately improving their productivity and manufacturing capabilities.

Environmental Assessment Approach for Mass Transit System in Uttaradit Province, Thailand

2024-05-31T21:27:36+07:00

This research aims to apply Environmental Assessment (EA) to the mass transit system of Uttaradit Province, Thailand. EA included Strategic Environmental Assessment (SEA) for the plan and program levels and Initial Environmental Examination (IEE) for the project level. SEA was scoped for the development and assessment of the appropriate alternatives for mass transit system development plan and mass transit system program. IEE was scoped for mass transit system selection resulted by SEA study. The appropriate alternative mass transit development plan derived from the SEA study was smart plan with positive impact score as 49.36%), compared to the conventional mass transit development plan and no plan with impact scored as 32.17%, was 18.4 %, respectively. Under the smart plan, the appropriate alternative assessed for the main mass transit system program derived by SEA study was the road with railway system with impact score of 36.64% compared with was railway system and road system with impact score as 33.90% and 29.46% respectively. After SEA assessment, IEE was undertaken for the minor mass transit system at project level defined as Minor 1, Minor 2 and Minor 3. Example of Minor 1 was assessed by comparison of Alternative 1,1 and Alternative 1.2. Herein, only environmental with social dimension was carried out for assessment. The impact scores of alternatives derived by IEE study were brought to combine with engineering and economic dimension for further consideration of the appropriate alternative.

Machine Learning Enabled-System for Screening Covid-19 Kind of Disease for Dense Population Sectors

2024-06-24T11:05:06+07:00

The Covid-19 and kind of pandemics flare-up has caused the world to suffer from health crisis and the situation in the developing countries is deplorable. The ever-growing cases are pushing the nation's well-being framework. The most effective way to protect yourself is to wear the face mask of your face in all areas of dense population. According to studies, wearing a mask reduces the chance of transmission. Cleanliness is a reference to practices that improve health and anticipation, specifically through orderliness, like hand washing. Hand washing is a great way of preventing transmission of virus which is transmitted through contact. A method of utilizing the human mind to build an environment that is solid and stable in a symbiotic environment that is strong and smart. A crossover model combining traditional and profound Deep Learning is going to be developed for mask recognition. In this paper we employ a training set to identify faces with a greater accuracy from live stream of camera. Infrared thermal sensors have been used for temperature estimation and safe following. Entryway regulators based on Raspberry Pi help security personnel avoid getting stuck in different locations, such as banks, entrances to schools, bank doors and medical clinic entrances. The validation factor of accuracy 0.97 and loss 0.02 were achieved.

Comparative investigation of performance for off-grid solar pump for further application in agriculture farms: A case study in Thailand

2024-06-10T09:39:33+07:00

Thailand's agricultural sector, with its substantial farming population, relies heavily on irrigation from small canals to sustain the cultivation of crops such as rice, corn, cassava, and sugarcane, which are replanted at various intervals throughout the year. This study evaluates the efficiency and economic viability of a direct-coupled solar water pumping system without storage, utilizing three distinct types of DC pumps—centrifugal, reciprocating, and submersible—each with a power rating of 750 W. The field tests were conducted concurrently under controlled conditions in a specific rural area in Thailand, with each pump connected to 3×340 W photovoltaic (PV) panels. The performance analysis revealed that the centrifugal pump achieved the highest efficiency, followed by the reciprocating pump, with the submersible pump ranking last. However, despite its lower efficiency, the submersible pump demonstrated the shortest payback period of 1.7 years, compared to 2.2 years for the centrifugal pump and 3.0 years for the reciprocating pump. These findings highlight the importance of selecting the appropriate pump type based on specific irrigation requirements and system design, as each pump offers unique advantages for different solar water pumping applications and areas.

Enhancing Small and Medium Enterprises in Phayao, Thailand: Socio-Economic Impact of Greenhouse Solar Drying for Andrographis Paniculata

2024-06-20T13:11:35+07:00

This study has examined the influence of varied drying air temperatures and the application potential of greenhouse solar drying (GSD) technologies in the drying kinetics of Andrographis paniculata during summer, rainy, and winter seasons. The results indicated that the trends for the predicted and observed values are highly analogous. Specifically, an increase in the coefficient of determination (R²) ranging from 0.9963 to 0.9992, a decrease in the root mean square error (RMSE) ranging from 0.0002 to 0.0022, and a reduction in chi-square values ranging from 0.0000 to 0.0005, demonstrated the feasibility and accuracy of all the models implemented. The moisture content analysis revealed a significant difference between GSD and natural convection drying methods. Using open sun drying, the final moisture content of Andrographis paniculata was reduced to approximately 12% after 7 days and further to a minimum of 9% after 14 days. In contrast, the GSD method decreased the final moisture content to less than 7% within just 2 days, highlighting its superior efficiency. From an economic perspective, the GSD system proved to be highly efficient and cost-effective. Despite the higher initial capital costs compared to natural convection systems, the life cycle costs of the GSD were lower than other previously studied solar drying systems. The economic feasibility was further supported by a swift payback period of 3.1 years, indicating a quick recovery of the initial investment relative to the system's lifespan. Additionally, the social impact assessment conducted using Social Return on Investment (SROI) analysis showed promising results. The SROI ratios for 2021, 2022, and 2023 were 2.09, 2.48, and 2.87 respectively, all well above the benchmark value of 1.00. This indicates a significant positive return, reflecting substantial social value and benefits to stakeholders and society. In conclusion, the GSD system not only offers superior drying performance and economic benefits but also generates substantial social value. Its implementation is both economically sustainable and socially beneficial, making it a highly advantageous technology for drying applications.

A Review of Metaheuristic Algorithms for Job Shop Scheduling

2024-05-30T14:38:25+07:00

Job shop scheduling (JSS) is a critical problem in the field of operations research and manufacturing, where the goal is to optimize the scheduling of jobs on machines to enhance productivity and efficiency. Combinatorial optimization problems like JSS present significant challenges due to their diverse applications and practical importance. In order to meet this challenge, metaheuristic algorithms have become extremely effective tools. They provide effective solutions that strike a balance between computational cost and solution quality. Given the Nondeterministic Polynomial time (NP)-hard nature of the problem, exact methods are often impractical for large instances, making metaheuristic approaches highly valuable due to their ability to find near-optimal solutions within reasonable computational times. The primary purpose of this review manuscript is to comprehensively analyze and synthesize the current state of research on metaheuristic algorithms applied to JSS. This review categorizes and summarizes contemporary metaheuristic methods such as harmony search, and ant colony optimization, alongside traditional techniques like genetic algorithms, simulated annealing, tabu search, and particle swarm optimization. The fundamental concepts, key components, and typical applications of metaheuristic algorithms are explored. The paper evaluates robustness, scalability, and adaptability of different methods to different problem instances and constraints, and performance metrics, highlighting their strengths and weaknesses. Additionally, this paper reviews recent advancements in hybrid and multi-objective metaheuristic methods aimed at balancing scheduling constraints and improving solution quality and convergence speed. By offering a critical evaluation of the literature, this manuscript aims to identify trends, gaps, and future research directions in the application of metaheuristic algorithms to JSS. The discussion includes an exploration of emerging techniques and their potential impact on the field, as well as the practical implications for industrial applications. The conclusion of the review highlights that while significant advancements have been made, there remain numerous opportunities for innovation and improvement in developing more robust, efficient, and adaptive metaheuristic algorithms. Future research should focus on hybrid approaches, real-time scheduling, and integrating machine learning techniques to further enhance the performance and applicability of these algorithms in complex, real-world JSS problems. This comprehensive review not only serves as a valuable resource for researchers and practitioners but also sets the stage for future innovations in the optimization of complex scheduling problems.