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Reliability Trade-Space Exploration Modelling for Satellite Anomalies Using Exponential Distribution
Full text linkThe delivery of critical communication, navigation, and earth observation services relies on the reliability of satellite subsystems. However, satellites can be affected by radiation and temperature extremes. Anomalies can cause the system to fail and stop working. This research aims to fix the lack of a high-performance reliability model compared to previous work by integrating trade-space exploration (TSE) techniques and an exponential reliability mathematical model. Data from the Seradata database was analyzed using MATLAB to check for issues with antennas, transponders, amplifiers, and batteries as the prominent components of satellite anomalies. Studies were carried out by building parametric (Weibull, Exponential, and Poisson) and non-parametric (Kaplan-Meier and Monte Carlo Simulation) models, and all were assessed. Results were measured using Root Mean Square Error (RMSE), revealing that the Exponential model performed most accurately compared to other mathematical models. After that, the TSE framework was applied to examine the Design Dependent Parameters (DDPs): reliability, design life, and system performance. The results conclude that the newly developed exponential-based reliability TSE model shows a promising outcome, with RMSE values of 34.2, 10.2, 19.6, and 19.9 compared to Nadirah’s model and Shazana’s model, which have RMSE values of 16.1, 49.7, 28.2, and 27.6 for antenna, transponder, amplifier, and battery, respectively.
ABSTRAK: Penghantaran Penghantaran perkhidmatan adalah penting dalam komunikasi, navigasi, dan pemerhatian bumi pada kebolehcapaian subsistem satelit. Walau bagaimanapun, satelit boleh terjejas oleh radiasi dan perubahan suhu ekstrem. Anomali boleh menyebabkan sistem gagal dan berhenti berfungsi. Penyelidikan ini bertujuan bagi memperbaiki kekurangan model kebolehcapaian berprestasi tinggi daripada kerja terdahulu dengan mengintegrasi teknik penerokaan ruang dagangan (TSE) dan model matematik kebolehcapaian Eksponen. Data daripada pangkalan data Seradata dianalisa menggunakan MATLAB bagi memeriksa isu antena, transponder, penguat, dan bateri sebagai komponen utama anomali satelit. Kajian dijalankan dengan membina model parametrik (Weibull, Eksponen, dan Poisson) dan non-parametrik (Kaplan-Meier dan Simulasi Monte Carlo). Dapatan kajian diukur menggunakan Ralat Kuadrat Purata Akar (RMSE), menunjukkan bahawa model Eksponen berfungsi paling tepat berbanding model matematik lain. Kemudian, rangka kerja TSE digunakan bagi mengkaji Parameter Bergantung Reka Bentuk (DDPs), yang merangkumi kebolehpercayaan, hayat reka bentuk, dan prestasi sistem. Dapatan kajian menunjukkan bahawa kebolehpercayaan model TSE berasaskan eksponensial yang baru dibangunkan menunjukkan hasil yang memberangsangkan di mana nilai RMSE adalah 34.2, 10.2, 19.6, dan 19.9 berbanding dengan model Nadirah dan model Shazana adalah 16.1, 49.7, 28.2, dan 27.6 untuk antena, transponder, penguat, dan bateri
Scalability and Cost Optimization in Load-Balanced Microservice Scheduling System
Full text linkMicroservice, a widely adopted architectural paradigm to overcome monolithic limitations, faces difficulties in efficient load balancing, scalability, and cost-effective deployment. To address these issues, we introduce a Container Microservice Load Balanced (CMLB) framework, which integrates the novel OEPTA algorithm. This framework aims to optimize microservice-based applications deployed on Docker within cloud environments. Common microservices scheduling strategies often grapple with load distribution challenges, resulting in suboptimal resource utilization. Concurrently, traditional containerization methods face difficulties reconciling trade-offs between scalability, deployment cost, and execution time. Our primary goal is to present a comprehensive solution that enhances the scalability, cost efficiency, and execution time of microservices deployment. This paper introduces a novel deployment framework for microservices, leveraging Docker for decentralized resource allocation across Microservice Controllers (MSCs). Additionally, a specialized algorithm is introduced to evaluate the cost, execution time, and availability aspects of microservice applications, enabling optimized resource allocation in a distributed manner. The evaluation results demonstrate that the CMLB framework, driven by the OEPTA algorithm, surpasses existing algorithms in achieving optimal scalability, cost efficiency, and execution times. This research provides a robust solution to enhance microservices deployment in cloud environments, effectively addressing key challenges in the field.
ABSTRAK: Mikroservis, sebuah paradigma seni bina yang diadaptasi secara meluas untuk mengatasi keterbatasan monolitik, menghadapi kesulitan dalam penyeimbangan beban yang cekap, skalabiliti, dan penyebaran kos efektif. Untuk mengatasi masalah ini, kami memperkenalkan rangka kerja Container Microservice Load Balanced (CMLB), yang mengintegrasikan algoritma OEPTA yang baru. Rangka kerja ini bertujuan untuk mengoptimumkan aplikasi berasaskan perkhidmatan mikroservis yang digunakan pada Docker dalam persekitaran awan. Strategi penjadualan mikroservis umumnya bergelut dengan cabaran pengagihan beban, yang menghasilkan penggunaan sumber daya yang kurang optimal. Pada masa yang sama, kaedah pengkontenaan tradisional menghadapi kesulitan dalam menyeimbangkan pertukaran antara skalabiliti, kos penggunaan, dan masa pelaksanaan. Matlamat utama kami adalah untuk membentangkan penyelesaian komprehensif yang meningkatkan skalabiliti, kos kecekapan, dan masa pelaksanaan dalam penggunaan mikroservis. Dalam makalah ini, kami memperkenalkan rangka kerja penggunaan yang baru untuk perkhidmatan mikroservis, dengan memanfaatkan Docker untuk peruntukan sumber terdesentralisasi merentas Pengawalan Perkhidmatan Mikroservis (MSCs). Selain itu, algoritma khusus diperkenalkan untuk menilai kos, masa pelaksanaan, dan ketersediaan aplikasi mikroservis, membolehkan peruntukan sumber dioptimumkan dalam cara yang diedarkan. Keputusan penilaian menunjukkan bahawa rangka kerja CMLB, didorong oleh algoritma OEPTA, mengatasi algoritma sedia ada dalam mencapai skalibiliti optimum, kecekapan kos, dan masa pelaksanaan. Penyelidikan ini memberikan penyelesaian yang teguh untuk meningkatkan penggunaan mikroservis dalam persekitaran awan, menangani cabaran utama dalam lapangan dengan berkesan
A Novel Induced Current Protection Scheme for Large-Scale Solar Photovoltaic Systems Using Early Streamer Emissions
Full text linkThe reliability and safety of large-scale solar photovoltaic systems (LSSPV) is paramount in harnessing renewable energy sources effectively. Given the increasing adoption of solar energy in Malaysian regions prone to lightning strikes, understanding and enhancing protection mechanisms is imperative. This study investigated an induced current protection system for LSSPV using an early streamer emission (ESE) air terminal in Malaysia. Two systems (ESE and Franklin lightning rod types) were employed in a 50 MWp PV power plant spanning 260 acres and were installed on the lightning arrester to ensure adequate protection. The Franklin rod type comprised 763 pieces and was constructed following the Council of Engineer standards (Thailand) standard. Meanwhile, the ESE lightning rod contained 68 pieces and was built following the NFC17102 standard (France). A 150 kA direct lightning impact was then simulated on the PV power plant using MATLAB/Simulink. Consequently, the ESE lightning protection system (LPS) effectively protected and prevented the lightning strike. The Franklin rod type's shading effects and installation costs (USD 10,026,800 vs. USD 8,026,800) were also more significant than the ESE rod type. These outcomes demonstrated that the ESE LPS was suitable for the PV power plant implementation. The findings of this study could also assist in optimizing the lightning protection technology for large-scale PV power plants.
ABSTRAK: Kebolehpercayaan dan keselamatan sistem fotovoltan suria berskala besar (LSSPV) adalah penting dalam memanfaatkan sumber tenaga boleh diperbaharui dengan berkesan. Memandangkan penggunaan tenaga suria yang semakin meningkat di kawasan Malaysia yang terdedah kepada panahan kilat, pemahaman dan meningkatkan mekanisme perlindungan adalah penting. Kajian ini menyiasat sistem perlindungan arus teraruh untuk LSSPV menggunakan terminal udara pelepasan aliran awal (ESE) di Malaysia. Dua sistem (jenis rod kilat ESE dan rod Franklin) telah digunakan dalam loji kuasa PV 50 MWp seluas 260 ekar dan dipasang pada penangkap kilat untuk memastikan perlindungan yang mencukupi. Jenis rod Franklin terdiri daripada 763 keping dan dibina mengikut piawaian Majlis Jurutera (Thailand). Sementara itu, rod kilat ESE mengandungi 68 keping dan dibina mengikut piawaian NFC17102 (Perancis). Kesan kilat langsung 150 kA kemudiannya disimulasikan pada jana kuasa PV menggunakan MATLAB/Simulink. Akibatnya, sistem perlindungan kilat (LPS) ESE melindungi dan menghalang serangan kilat dengan berkesan. Kesan teduhan dan kos pemasangan (USD 10,026,800 lwn. USD 8,026,800) jenis rod Franklin juga lebih ketara daripada jenis rod ESE. Hasil ini menunjukkan bahawa LPS ESE sesuai untuk pelaksanaan jana kuasa PV. Penemuan kajian ini juga boleh membantu dalam mengoptimumkan teknologi perlindungan kilat untuk jana kuasa PV berskala besar
4D Radar Imaging and Camera Fusion for Road Crossing Detection and Classification Using Deep Learning
Full text linkThis paper presents the development of an object detection and classification system for road crossing areas, integrating 4D radar imaging and a mono-camera dataset with a deep-learning neural network. The system utilizes deep neural networks implemented via Keras and TensorFlow to detect and classify multiple targets, including pedestrians, cars, buses, and trucks. At the core of this work is Retina-4F, a multi-chip radar imaging system developed by Smart Radar System, which offers high-resolution object detection and localization capabilities. Retina-4F provides real-time 4D information on detected objects, operating in a cascading architecture with three transmitters and four receivers per chip. Two road-crossing scenes were simulated to collect data, generating a point cloud dataset labeled with target classes for neural network training and testing. Data from two main sensors—Retina-4F and a mono-camera—were pre-processed using DBSCAN and YOLOv7 for enhanced accuracy. Operating at 77 GHz, Retina-4F was tested in two road environments, generating a dataset with approximately 10,000 frames. The deep learning model demonstrated an accuracy of 84% in classifying multiple targets, including cars, pedestrians, buses, and trucks. The fusion of radar point cloud data with visual sensor data proved effective, showing strong results in distinguishing target types.
ABSTRAK: Kertas ini membentangkan pembangunan sistem pengesanan dan pengelasan objek untuk kawasan lintasan jalan raya, menggabungkan pengimejan radar 4D dan set data mono-kamera dengan rangkaian neural pembelajaran mendalam. Sistem ini menggunakan rangkaian neural mendalam yang dilaksanakan melalui Keras dan TensorFlow untuk mengesan dan mengelaskan pelbagai sasaran, termasuk pejalan kaki, kereta, bas, dan trak. Inti daripada kajian ini adalah Retina-4F, sistem pengimejan radar berbilang cip yang dibangunkan oleh Smart Radar System, yang menawarkan keupayaan pengesanan objek dan penentuan lokasi resolusi tinggi. Retina-4F menyediakan maklumat 4D masa nyata mengenai objek yang dikesan, beroperasi dengan tiga pemancar dan empat penerima bagi setiap cip dalam seni bina kaskad. Dua adegan lintasan jalan disimulasikan untuk mengumpul data, menghasilkan set data awan titik yang dilabel dengan kelas sasaran untuk latihan dan ujian rangkaian neural. Data daripada dua sensor utama—Retina-4F dan mono-kamera—dipra-proses menggunakan DBSCAN dan YOLOv7 untuk meningkatkan ketepatan. Beroperasi pada 77 GHz, Retina-4F diuji dalam dua persekitaran jalan yang berbeza, menghasilkan set data dengan kira-kira 10,000 bingkai. Model pembelajaran mendalam menunjukkan ketepatan sebanyak 84% dalam mengelaskan pelbagai sasaran, termasuk kereta, pejalan kaki, bas, dan trak. Penggabungan data awan titik radar dengan data sensor visual terbukti berkesan, menunjukkan hasil yang kuat dalam membezakan antara jenis sasaran
Modified COST-235 Empirical Pathloss Model for Agricultural WSN Using Particle Swarm Optimization
Full text linkThe increasing demand for agricultural products yearly encourages farmers to seek solutions to migrate from conventional farming to smart and precise farming by utilizing technological advances such as implementing wireless sensor networks (WSN). Unlike conventional farming, this technology is believed to provide many advantages, including low cost, high efficiency, optimized land use, and high productivity results. However, this system is highly dependent on the availability of network interconnection where the bottleneck is the instability of signal strength and path loss, especially for radio wave propagation from the transmitter (Tx) in the form of sensors to the receiver (Rx) in the form of data processors where its performance depends on the distance, agricultural, environmental conditions, and surrounding vegetation. This paper explicitly examines and analyzes radio wave propagation modeling for measuring radio frequency (RF) signal strength in local agriculture's 2.4 GHz WSN system, such as Adan rice, corn, and peanuts. The particle-swarm-optimization (PSO) method is used to modify empirical path loss models such as Weissberger, ITU-vegetation, COST-235, Egli, and FITU-R, which also involve the influence of rain attenuation. Several other factors are also considered in the evaluation and analysis, i.e., the planting period of agricultural crops (seedlings, growth, and maturity), vegetation depth, and the height of the Tx-Rx antenna from the ground. The results of the experimental evaluation show that the PL COST-235 model continues to be optimized using the PSO method because it has the lowest RMSE both in conditions without and with rain attenuation, which are 23.30 and 9.33, respectively. Meanwhile, after the selected model is optimized using the PSO method, the RMSE for both conditions becomes 2.49 and 5.29.
ABSTRAK: Permintaan yang semakin meningkat terhadap produk pertanian setiap tahun mendorong para petani untuk mencari penyelesaian bagi beralih daripada pertanian konvensional kepada pertanian pintar dan tepat dengan memanfaatkan kemajuan teknologi seperti penggunaan rangkaian sensor tanpa wayar (WSN). Berbeza dengan pertanian konvensional, teknologi ini dipercayai memberikan banyak kelebihan, termasuk kos yang rendah, kecekapan yang tinggi, pengoptimuman penggunaan tanah, dan hasil produktiviti yang tinggi. Namun begitu, sistem ini sangat bergantung kepada ketersediaan rangkaian interkoneksi di mana kelemahan utamanya adalah ketidakstabilan kekuatan isyarat dan kehilangan laluan (path loss), terutamanya bagi penyebaran gelombang radio dari pemancar (Tx) berbentuk sensor ke penerima (Rx) berbentuk pemproses data, yang prestasinya bergantung kepada jarak, keadaan persekitaran pertanian, dan tumbuh-tumbuhan di sekeliling. Kajian ini secara khusus meneliti dan menganalisis pemodelan penyebaran gelombang radio untuk mengukur kekuatan isyarat frekuensi radio (RF) dalam sistem WSN 2.4 GHz di pertanian tempatan seperti padi Adan, jagung, dan kacang tanah. Kaedah pengoptimuman kawanan zarah (particle-swarm-optimization, PSO) digunakan untuk mengubah suai model kehilangan laluan empirikal seperti Weissberger, ITU-vegetation, COST-235, Egli, dan FITU-R, yang turut melibatkan pengaruh pelemahan hujan. Beberapa faktor lain juga dipertimbangkan dalam penilaian dan analisis ini, seperti tempoh penanaman tanaman pertanian (anak benih, pertumbuhan, dan kematangan), kedalaman tumbuh-tumbuhan, dan ketinggian antena Tx-Rx dari permukaan tanah. Hasil penilaian eksperimen menunjukkan bahawa model PL COST-235 terus dioptimumkan menggunakan kaedah PSO kerana ia mempunyai nilai RMSE paling rendah dalam kedua-dua keadaan tanpa dan dengan pelemahan hujan, iaitu masing-masing 23.30 dan 9.33. Sementara itu, selepas model yang dipilih dioptimumkan menggunakan kaedah PSO, nilai RMSE bagi kedua-dua keadaan menjadi 2.49 dan 5.29
Comparative Analysis of Vision Transformers and CNN Models for Driver Fatigue Classification
Full text linkThis study provides a comprehensive evaluation of Convolutional Neural Network (CNN) and Vision Transformer (ViT) models for driver fatigue classification, a critical issue in road safety. Using a custom driving behavior dataset, state-of-the-art CNN and ViT architectures, including VGG16, EfficientNet, MobileNet, Inception, DenseNet, ResNet, ViT, and Swin Transformer, were analyzed in this study to determine the best model for practical driver fatigue monitoring systems. Performance metrics such as accuracy, F1-score, training time, inference time, and frames per second (fps) were assessed across different hardware platforms, including a high-performance workstation, Raspberry Pi 5, and a desktop with a Graphic Processing Unit (GPU). Results demonstrate that CNN models, particularly VGG16, achieve the best balance between accuracy and efficiency, with an F1-score of 0.97 and 77.00 fps on a desktop. On the other hand, Swin V2S outperforms all models in terms of accuracy, achieving an F1-score of 0.99 and 61.18 fps on a GPU, although it exhibits limited efficiency on embedded systems. This study significantly contributes by providing practical recommendations for selecting models based on performance needs and hardware constraints, highlighting the suitability of ViTs for high-computation environments. The findings support the development of more efficient driver fatigue monitoring systems, offering practical implications for enhancing road safety and reducing traffic accidents.
ABSTRAK: Kajian ini merupakan penilaian komprehensif terhadap model Konvolusi Rangkaian Neural (CNN) dan Transformer Penglihatan (ViT) bagi pengelasan keletihan pemandu, iaitu satu isu kritikal dalam keselamatan jalan raya. Menggunakan set data tingkah laku pemanduan tersuai, seni bina terkini CNN dan ViT, termasuk VGG16, EfficientNet, MobileNet, Inception, DenseNet, ResNet, ViT dan Transformer Swin dianalisa dalam kajian ini bagi menentukan model terbaik bagi sistem pemantauan keletihan pemandu yang praktikal. Metrik prestasi seperti ketepatan, skor F1, masa latihan, masa inferens, dan bingkai sesaat (fps) telah dinilai merentasi pelbagai platfom perkakasan, termasuk stesen kerja berprestasi tinggi, Raspberry Pi 5, dan komputer meja dengan Unit Pemprosesan Grafik (GPU). Dapatan kajian menunjukkan bahawa model CNN, khususnya VGG16, mencapai keseimbangan terbaik antara ketepatan dan kecekapan, dengan skor F1 sebanyak 0.97 dan 77.00 fps pada komputer meja. Sebaliknya, Swin V2S mengatasi semua model dari segi ketepatan, mencapai skor F1 sebanyak 0.99 dan 61.18 fps pada GPU, walaupun menunjukkan kecekapan yang terhad pada sistem terbenam. Kajian ini memberikan sumbangan yang signifikan dengan menyediakan cadangan praktikal bagi pemilihan model berdasarkan keperluan prestasi dan kekangan perkakasan, serta menonjolkan kesesuaian ViT bagi persekitaran berkomputasi tinggi. Penemuan ini menyokong pembangunan sistem pemantauan keletihan pemandu yang lebih cekap, dengan implikasi praktikal bagi meningkatkan keselamatan jalan raya dan mengurangkan kemalangan
Autoencoder Artificial Neural Network Model for Air Pollution Index Prediction
Full text linkAir pollution, a significant global challenge driven by industrialization, urbanization, and population growth, is caused by the emission of harmful gases, particulates, and biological molecules into the atmosphere, posing serious risks to health and the environment. Key sources include power plants, industrial activities, vehicles, and residential heating. Thus, effective air quality monitoring and forecasting are crucial to mitigating the adverse impacts of pollution. This paper presents shallow and deep sparse autoencoder artificial neural network models to improve the prediction of the Air Pollution Index (API) in Perak Darul Ridzuan, Malaysia, as a case study. The results show that the deep sparse autoencoder achieves better prediction accuracy with and values of 0.1474 and 0.8331, respectively, compared to 0.1515 and 0.8300 for the shallow sparse autoencoder. The performance of these autoencoder models is also compared with other models, such as feedforward artificial neural networks (FANN) and principal component analysis (PCA). The findings confirm that both autoencoder models enhance API prediction accuracy, with the deep sparse autoencoder emerging as the optimal model, highlighting the potential of deep learning in improving air quality prediction.
ABSTRAK: Pencemaran udara, merupakan satu cabaran global yang didorong oleh perindustrian, urbanisasi pesat, dan pertumbuhan populasi, adalah disebabkan oleh pelepasan gas, partikel, dan molekul biologi merbahaya ke atmosfera, menimbulkan risiko serius kepada kesihatan dan alam sekitar. Sumber utama termasuk loji janakuasa, aktiviti industri, kenderaan, dan pemanasan kediaman. Oleh itu pemantauan dan ramalan kualiti udara penting bagi mengurangkan kesan buruk pencemaran. Kajian ini membentangkan model rangkaian neural tiruan pengauto kod jarang ‘cetek’ dan pengauto kod jarang ‘dalam’ memperbaiki ramalan Indeks Pencemaran Udara (API) di negeri Perak Darul Ridzuan, Malaysia sebagai kes kajian. Dapatan kajian menunjukkan bahawa pengautokod jarang ‘dalam’ mencapai ketepatan ramalan lebih baik, dengan nilai MSE dan R2 masing-masing sebanyak 0.1474 dan 0.8331, berbanding 0.1515 dan 0.8300 bagi pengautokod jarang ‘cetek’. Prestasi model pengautokod ini juga dibandingkan dengan model lain, seperti rangkaian neural tiruan suapan hadapan (FANN) dan analisis komponen utama (PCA). Hasil kajian mengesahkan bahawa kedua-dua model pengautokod meningkatkan ketepatan ramalan API, dengan pengautokod jarang ‘dalam’ muncul sebagai model paling optimum, menonjolkan potensi pembelajaran mendalam ‘dalam’ meningkatkan ramalan kualiti udara
Wearable Textile Patch DSSRS Antenna for Body Tumors Detection with Reduced SAR
Full text linkThe purpose of this study is to present a lightweight wearable (jeans) monopole antenna configuration for body area network (BAN) communication, breast and head tumors detections with back lobe reduction (i.e., low SAR), and it does so without introducing any special methodologies like as, AMC, EBG, HIS. The planned antenna has dual symmetrical slots as well as a ring-shaped slot (DSSRS) at the top, and it is in the form of a radiating rectangular patch with a ground plane. The design procedure has been finished with the help of CST MWS, and the next step will be to fine-tune the parameters of the antenna structure to achieve resonance at the ISM band (5.79 GHz). Testing for BAN, breast, and brain tumor detection was done using this prototype. With the proper impedance matching, the antenna achieves an operational bandwidth of 5.798 GHz (5.739–5.865 GHz), 5.77 GHz (5.715–5.838 GHz), 5.77 GHz (5.718–5.843 GHz) and 5.78 GHz (5.725–5.834 GHz), with an overall peak gain of 8.18 dBi, 7.69 dBi, 5.73 dBi, and 4.59 dBi; when proposed antenna placed on the free space, on the body, on the breast, and the head respectively. The suggested antenna meets the specific absorption rate (SAR) standards given by the FCC (1 gm) and the ICNIRP (10 gm).
ABSTRAK: Kajian ini bertujuan untuk membentangkan konfigurasi antena monopole ringan boleh pakai (jenis jeans) untuk komunikasi rangkaian kawasan badan (BAN), pengesanan tumor payudara dan kepala dengan pengurangan lobus belakang (iaitu, SAR rendah), tanpa menggunakan metodologi khas seperti AMC, EBG, atau HIS. Antena yang dicadangkan mempunyai dua slot simetri (dual symmetrical slots) serta slot berbentuk cincin (DSSRS) di bahagian atas, dan berbentuk patch segi empat tepat yang memancar dengan satah tanah. Prosedur reka bentuk telah diselesaikan dengan bantuan perisian CST MWS, dan langkah seterusnya adalah untuk menyesuaikan parameter struktur antena bagi mencapai resonans pada jalur ISM (5.79 GHz). Ujian untuk BAN, pengesanan tumor payudara, dan tumor otak telah dijalankan menggunakan prototaip ini. Dengan padanan impedans yang betul, antena ini mencapai lebar jalur operasi sebanyak 5.798 GHz (5.739–5.865 GHz), 5.77 GHz (5.715–5.838 GHz), 5.77 GHz (5.718–5.843 GHz), dan 5.78 GHz (5.725–5.834 GHz), dengan pencapaian keuntungan puncak keseluruhan sebanyak 8.18 dBi, 7.69 dBi, 5.73 dBi, dan 4.59 dBi; apabila antena yang dicadangkan diletakkan di ruang bebas, pada badan, pada payudara, dan pada kepala masing-masing. Antena yang dicadangkan memenuhi piawaian kadar penyerapan spesifik (SAR) yang ditetapkan oleh FCC (1 gm) dan ICNIRP (10 gm
Safety Enhancement of Portable Oil Spill Skimmer (POSS) via Computational Fluid Dynamics for Liquid Sloshing Analysis
Full text linkThe Portable Oil Spill Skimmer (POSS) was designed and developed due to several disadvantages of the current methods of Oil Spill Response and Recovery (OSRR). The POSS was designed as a complementary method to aid the OSRR tasks. However, during the POSS maneuverability testing, the POSS experiences instability when moving in different directions. The imbalance occurs when there is the presence of oil in the oil tank. Based on the literature study, the liquid sloshing effect was the reason why the POSS experiences instability. Thus, this research aims to analyse the impact of liquid sloshing in an oil tank and the implementation of baffles to reduce the effect. The analysis was conducted using SolidWorks Flow Simulation to simulate the liquid sloshing in the oil tank. The simulation was conducted in two situations, with and without baffles, to compare the results. According to the obtained results, with the implementation of 3 baffles, the sloshing effect was reduced to 392 N of torque force from 1195.43 N without baffles. The reduction was significant as the sloshing effect cannot be eliminated, thus the torque force of 392 N was enough to minimise the stability issue of the POSS.
ABSTRAK: Penapis Tumpahan Minyak Mudah Alih (POSS) telah direka bentuk dan difabrikasi kerana terdapat beberapa kelemahan kaedah semasa iaitu Tindak Balas dan Pemulihan Tumpahan Minyak (OSRR). POSS direka bentuk sebagai kaedah pelengkap bagi membantu operasi OSRR. Walau bagaimanapun, semasa ujian kebolehgerakan POSS, ia mengalami ketidakstabilan gerakan arah berbeza. Ketidakseimbangan ini berlaku apabila terdapat minyak dalam tangki minyak. Berdasarkan kajian, POSS mengalami ketidakstabilan disebabkan oleh kesan percikan cecair (liquid sloshing). Oleh itu, kajian ini bertujuan bagi menganalisis kesan percikan minyak (oil sloshing) dalam tangki minyak dan mengkaji keberkesanan pelaksanaan penyekat dalaman (baffles) bagi mengurangkan kesan percikan. Analisis dijalankan dengan menggunakan Perisian Simulasi Aliran SolidWorks bagi mensimulasikan percikan cecair dalam tangki minyak. Simulasi dijalankan dalam dua keadaan, dengan dan tanpa penyekat dalaman bagi membandingkan keputusan. Dapatan kajian mendapati melalui pelaksanaan 3 penyekat dalaman (baffles), kesan percikan telah berjaya dikurangkan kepada 392 N daya kilas (torque force) berbanding 1195.43 N tanpa menggunakan penyekat dalaman. Pengurangan ini adalah ketara kerana kesan percikan tidak dapat dihapuskan sepenuhnya. Oleh itu, daya kilas 392 N adalah cukup bagi meminimumkan isu kestabilan POSS
Electrochemical Sensing of Nicotine Using Laser-Induced Graphene Screen-Printed Electrode
Full text linkNicotine is one of the major addictive substances in tobacco plants, which caused a global pandemic. Rapid detection of nicotine is crucial to allow quick identification of harmful substances that will cause significant health risks, especially with the recent rise in electronic cigarettes. Since smoking cessation programs are typically limited to screening, awareness, consultation, medication, and follow-up activities, there is a need for a device to check the nicotine level in former smokers at the end of the programs. However, most of the current nicotine detection is based on chromatography technology, which involves complicated sample pre-treatment and bulky and expensive instruments. Thus, screen-printing technology employing electrochemical detection is a promising solution as it offers a simple and portable setup for nicotine detection. Yet, conventional screen-printed electrodes (SPE) have relatively low sensitivity and need modification to improve the electrode material. Therefore, this work aims to investigate the performance of laser-induced graphene (LIG) as SPE-modified electrodes to detect the presence of nicotine through electrochemical measurements. A finite element simulation was conducted to investigate laser power's effect on the induced graphene's quality. The CO2 laser with 3W laser power, Dots per inch (DPI) of 1200, and a laser speed of 13% was used to fabricate the LIG sensor on a Kapton substrate. Material characterizations such as SEM, EDX, and Raman spectra were performed on the fabricated LIG-SPE to confirm the presence of LIG. Cyclic voltammetry (CV) measurement was done using 0.1M [Fe (CN)6]3-/4- and 0.1M KCL to find the suitable scan rates. At a fixed scan rate of 50 mV/s, the sensor's performance was analyzed using 0.1M of nicotine with 3 different phosphate buffer solutions (PBS) of pH 5, pH 7, and pH 9 at different nicotine concentrations. Nicotine with PBS pH 5 solution was found to be the optimum measured solution, with the value obtained for R² having the highest value of 0.9988 and the lowest LOD of 4.2183 ?M. The proposed electrochemical sensing of nicotine using a laser-induced graphene screen printed electrode can detect nicotine with high linearity at different pH levels of PBS buffer solution.
ABSTRAK: Nikotin adalah salah satu bahan ketagihan utama dalam tumbuhan tembakau yang menyebabkan pandemik global. Pengesanan cepat nikotin adalah penting bagi membolehkan pengecaman cepat bahan merbahaya yang menyebabkan risiko kesihatan ketara terutamanya dengan peningkatan rokok elektronik pada masa sekarang. Memandangkan program berhenti merokok biasanya terhad kepada pemeriksaan, kesedaran, perundingan, ubat-ubatan dan aktiviti susulan, terdapat keperluan bagi peranti memeriksa tahap nikotin dalam bekas perokok pada akhir program. Walau bagaimanapun, kebanyakan pengesanan nikotin semasa adalah berdasarkan teknologi kromatografi, di mana melibatkan sampel pra-rawatan rumit, instrumen besar dan mahal. Oleh itu, teknologi percetakan skrin yang menggunakan pengesanan eletrokimia adalah penyelesaian bermakna kerana ia menawarkan persediaan mudah dan mudah alih bagi mengesan nikotin. Namun, skrin-cetakan elektrod konvensional (SPE) mempunyai sensitiviti rendah dan memerlukan pengubahsuaian bagi menambah baik bahan elektrod. Oleh itu, kajian ini adalah untuk menyiasat prestasi laser graphen teraruh (LIG) sebagai elektrod SPE yang diubah suai bagi mengesan kehadiran nikotin melalui pengukuran elektrokimia. Simulasi unsur terhingga telah dijalankan bagi melihat kesan kuasa laser ke atas kualiti graphen teraruh. Laser CO? dengan kuasa laser 3W, dot per inci (DPI) sebanyak 1200, dan kelajuan laser sehingga 13% telah digunakan bagi mengfabrikasi pengimbas LIG pada substrat Kapton. Pencirian bahan sperti SEM, EDX, dan spektrum Raman dilakukan pada LIG-SPE yang direka bagi mengesahkan kehadiran LIG. Pengukuran voltametri kitaran (CV) dilakukan menggunakan 0.1M [Fe (CN)6]3-/4- dan 0.1M KCL bagi mencari kadar imbasan yang sesuai. Pada kadar imbasan tetap 50 mV/s, prestasi pengimbas dianalisa menggunakan 0.1M nikotin dengan 3 larutan penimbal fosfat (PBS) berbeza pH 5, pH 7, dan pH 9 pada kepekatan nikotin berbeza. Nikotin dengan larutan PBS pH 5 didapati sebagai larutan optimum, dengan nilai R² tertinggi 0.9988 dan LOD terendah 4.2183 ?M. Kesimpulannya, pengimbas elektrokimia nikotin menggunakan laser elektrod skrin bercetak graphen teraruh dapat mengesan nikotin dengan pemalaran tinggi pada pH larutan penimbal PBS yang berbeza