Displaying publications 1 - 20 of 73 in total

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  1. Rekabentuk dan implementasi sistem automasi pembotolan MAPS menggunakan Programmable Logic Controller, PLC
    Riza Sulaiman, Prabuwono AS, Kurniawan D, Syaimak Abdul Shukor
    Sains Malaysiana, 2009;38.
    Kertas kerja ini membincangkan rekabentuk dan implementasi Programmable Logic Controller (PLC) untuk aplikasi miniatur pembuatan pembotolan (Modular Automation Production System - MAPS). PLC digunakan untuk menjalankan sistem supaya bekerja secara automatik dan digunakan untuk aplikasi sistem yang berulang. Penggunaan PLC dalam industri bertujuan meminimumkan kos pengeluaran, meningkatkan produktiviti, meningkatkan kualiti dan kebolehpercayaan sistem. Rekabentuk dan implementasi sistem automasi dilakukan dengan menggunakan MAPS. MAPS adalah suatu sistem modular yang dibangunkan dengan menggunakan beberapa stesen menjadi sistem terintegrasi. Di dalam kajian ini, MAPS digunakan sebagai miniatur pengeluaran pembotolan yang sebenar dan merupakan integrasi beberapa sistem iaitu PLC, pengesan, pneumatik, mekanik, elektronik dan sistem kawalan.
    Matched MeSH terms: Automation
  2. Fulltext Parameters-tuning of PID controller for automatic voltage regulators using the African buffalo optimization
    Odili JB, Mohmad Kahar MN, Noraziah A
    PLoS One, 2017;12(4):e0175901.
    PMID: 28441390 DOI: 10.1371/journal.pone.0175901
    In this paper, an attempt is made to apply the African Buffalo Optimization (ABO) to tune the parameters of a PID controller for an effective Automatic Voltage Regulator (AVR). Existing metaheuristic tuning methods have been proven to be quite successful but there were observable areas that need improvements especially in terms of the system's gain overshoot and steady steady state errors. Using the ABO algorithm where each buffalo location in the herd is a candidate solution to the Proportional-Integral-Derivative parameters was very helpful in addressing these two areas of concern. The encouraging results obtained from the simulation of the PID Controller parameters-tuning using the ABO when compared with the performance of Genetic Algorithm PID (GA-PID), Particle-Swarm Optimization PID (PSO-PID), Ant Colony Optimization PID (ACO-PID), PID, Bacteria-Foraging Optimization PID (BFO-PID) etc makes ABO-PID a good addition to solving PID Controller tuning problems using metaheuristics.
    Matched MeSH terms: Automation/methods
  3. Fulltext Development of an automated testing tool for Java® program
    Kamal, Z.Z., Daud, A.H.M., Ashidi, M.I.N., Fadel, J.K.M.
    ASM Science Journal, 2007;1(2):87-100.
    MyJurnal
    Covering as much as 25% to 35% of the development cost, software testing is an integral part of the software development lifecycle. Despite its importance, the current software testing practice is still based on highly manual processes from the generation of test cases (i.e. from specifications) up to the actual execution of the test. These manually generated tests are sometimes executed using ad hoc approaches, typically requiring the construction of a test driver for the particular application under test. In addition, test engineers are also under pressure to test increasing lines of code in order to meet market demands for more software functionalities. While there are significant proliferations of helpful testing tools or research prototypes in the market, much of them do not adequately provide the right level of abstraction and automation as required by test engineers. In order to facilitate and address some of the aforementioned issues, an automated testing tool was developed, called SFIT, based on Java® technology. This paper describes the development, implementation and evaluation of SFIT. Two case studies involving the robustness assessment of an adder module and a Linda-based distributed shared memory implementation are described in order to demonstrate the applicability of SFIT as a helpful automated testing tool.
    Matched MeSH terms: Automation
  4. Fulltext An Interoperable Component-Based Architecture for Data-Driven IoT System
    Lo SK, Liew CS, Tey KS, Mekhilef S
    Sensors (Basel), 2019 Oct 09;19(20).
    PMID: 31600904 DOI: 10.3390/s19204354
    The advancement of the Internet of Things (IoT) as a solution in diverse application domains has nurtured the expansion in the number of devices and data volume. Multiple platforms and protocols have been introduced and resulted in high device ubiquity and heterogeneity. However, currently available IoT architectures face challenges to accommodate the diversity in IoT devices or services operating under different operating systems and protocols. In this paper, we propose a new IoT architecture that utilizes the component-based design approach to create and define the loosely-coupled, standalone but interoperable service components for IoT systems. Furthermore, a data-driven feedback function is included as a key feature of the proposed architecture to enable a greater degree of system automation and to reduce the dependency on mankind for data analysis and decision-making. The proposed architecture aims to tackle device interoperability, system reusability and the lack of data-driven functionality issues. Using a real-world use case on a proof-of-concept prototype, we examined the viability and usability of the proposed architecture.
    Matched MeSH terms: Automation
  5. Beamforming technique for investigation of lateral variability at geotechnical sites
    Norfarah Nadia Ismail, Joh SH, Raja Hassanul Musa Raja Ahmad
    Sains Malaysiana, 2012;41:1621-1627.
    A beamformer in seismology is a signal receptor with a series of geophones, in which a beam of elastic waves is formed like a light beam by adjusting signal delays at individual geophones. Recently, beamforming has extended its applications to surface-wave measurement. In surface-wave measurement, beamforming provides unique advantages over other surface-wave methods, such as full automation in data analysis as well as directional signal reception to minimize scattered noise and multiple reflections in signals. However, certain defects depreciate the value of beamforming in terms of its practicality and feasibility. These include the requirement of having many receivers and the loss of small wavelength data due to spatial aliasing. It leads to insensitivity in identification of lateral variability, which creates the problem of having to smooth out geologic features and complexities like folding, faults and fractures. In this paper, advances in the refinement of beamforming were described on two counts: improvement of sensitivity in identification of lateral variability and recovery of aliased wave numbers, which enables evaluation of shallow material. On the passage to refinement, synthetic waveforms for typical layering systems were generated to figure out characteristics of beamformer velocities in comparison with SASW velocities and theoretical normal-mode velocities.
    Matched MeSH terms: Automation
  6. An automated decision-support system for non-proliferative diabetic retinopathy disease based on MAs and HAs detection
    Saleh MD, Eswaran C
    Comput Methods Programs Biomed, 2012 Oct;108(1):186-96.
    PMID: 22551841 DOI: 10.1016/j.cmpb.2012.03.004
    Diabetic retinopathy (DR) has become a serious threat in our society, which causes 45% of the legal blindness in diabetes patients. Early detection as well as the periodic screening of DR helps in reducing the progress of this disease and in preventing the subsequent loss of visual capability. This paper provides an automated diagnosis system for DR integrated with a user-friendly interface. The grading of the severity level of DR is based on detecting and analyzing the early clinical signs associated with the disease, such as microaneurysms (MAs) and hemorrhages (HAs). The system extracts some retinal features, such as optic disc, fovea, and retinal tissue for easier segmentation of dark spot lesions in the fundus images. That is followed by the classification of the correctly segmented spots into MAs and HAs. Based on the number and location of MAs and HAs, the system quantifies the severity level of DR. A database of 98 color images is used in order to evaluate the performance of the developed system. From the experimental results, it is found that the proposed system achieves 84.31% and 87.53% values in terms of sensitivity for the detection of MAs and HAs respectively. In terms of specificity, the system achieves 93.63% and 95.08% values for the detection of MAs and HAs respectively. Also, the proposed system achieves 68.98% and 74.91% values in terms of kappa coefficient for the detection of MAs and HAs respectively. Moreover, the system yields sensitivity and specificity values of 89.47% and 95.65% for the classification of DR versus normal.
    Matched MeSH terms: Automation*
  7. Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images
    Acharya UR, Sree SV, Muthu Rama Krishnan M, Krishnananda N, Ranjan S, Umesh P, et al.
    Comput Methods Programs Biomed, 2013 Dec;112(3):624-32.
    PMID: 23958645 DOI: 10.1016/j.cmpb.2013.07.012
    Coronary Artery Disease (CAD), caused by the buildup of plaque on the inside of the coronary arteries, has a high mortality rate. To efficiently detect this condition from echocardiography images, with lesser inter-observer variability and visual interpretation errors, computer based data mining techniques may be exploited. We have developed and presented one such technique in this paper for the classification of normal and CAD affected cases. A multitude of grayscale features (fractal dimension, entropies based on the higher order spectra, features based on image texture and local binary patterns, and wavelet based features) were extracted from echocardiography images belonging to a huge database of 400 normal cases and 400 CAD patients. Only the features that had good discriminating capability were selected using t-test. Several combinations of the resultant significant features were used to evaluate many supervised classifiers to find the combination that presents a good accuracy. We observed that the Gaussian Mixture Model (GMM) classifier trained with a feature subset made up of nine significant features presented the highest accuracy, sensitivity, specificity, and positive predictive value of 100%. We have also developed a novel, highly discriminative HeartIndex, which is a single number that is calculated from the combination of the features, in order to objectively classify the images from either of the two classes. Such an index allows for an easier implementation of the technique for automated CAD detection in the computers in hospitals and clinics.
    Matched MeSH terms: Automation*
  8. Fulltext Optimal path test data generation based on hybrid negative selection algorithm and genetic algorithm
    Mohi-Aldeen SM, Mohamad R, Deris S
    PLoS One, 2020;15(11):e0242812.
    PMID: 33253281 DOI: 10.1371/journal.pone.0242812
    Path testing is the basic approach of white box testing and the main approach to solve it by discovering the particular input data of the searching space to encompass the paths in the software under test. Due to the increasing software complexity, exhaustive testing is impossible and computationally not feasible. The ultimate challenge is to generate suitable test data that maximize the coverage; many approaches have been developed by researchers to accomplish path coverage. The paper suggested a hybrid method (NSA-GA) based on Negative Selection Algorithm (NSA) and Genetic Algorithm (GA) to generate an optimal test data avoiding replication to cover all possible paths. The proposed method modifies the generation of detectors in the generation phase of NSA using GA, as well as, develops a fitness function based on the paths' prioritization. Different benchmark programs with different data types have been used. The results show that the hybrid method improved the coverage percentage of the programs' paths, even for complicated paths and its ability to minimize the generated number of test data and enhance the efficiency even with the increased input range of different data types used. This method improves the effectiveness and efficiency of test data generation and maximizes search space area, increasing percentage of path coverage while preventing redundant data.
    Matched MeSH terms: Automation*
  9. Systematic Review of an Automated Multiclass Detection and Classification System for Acute Leukaemia in Terms of Evaluation and Benchmarking, Open Challenges, Issues and Methodological Aspects
    Alsalem MA, Zaidan AA, Zaidan BB, Hashim M, Albahri OS, Albahri AS, et al.
    J Med Syst, 2018 Sep 19;42(11):204.
    PMID: 30232632 DOI: 10.1007/s10916-018-1064-9
    This study aims to systematically review prior research on the evaluation and benchmarking of automated acute leukaemia classification tasks. The review depends on three reliable search engines: ScienceDirect, Web of Science and IEEE Xplore. A research taxonomy developed for the review considers a wide perspective for automated detection and classification of acute leukaemia research and reflects the usage trends in the evaluation criteria in this field. The developed taxonomy consists of three main research directions in this domain. The taxonomy involves two phases. The first phase includes all three research directions. The second one demonstrates all the criteria used for evaluating acute leukaemia classification. The final set of studies includes 83 investigations, most of which focused on enhancing the accuracy and performance of detection and classification through proposed methods or systems. Few efforts were made to undertake the evaluation issues. According to the final set of articles, three groups of articles represented the main research directions in this domain: 56 articles highlighted the proposed methods, 22 articles involved proposals for system development and 5 papers centred on evaluation and comparison. The other taxonomy side included 16 main and sub-evaluation and benchmarking criteria. This review highlights three serious issues in the evaluation and benchmarking of multiclass classification of acute leukaemia, namely, conflicting criteria, evaluation criteria and criteria importance. It also determines the weakness of benchmarking tools. To solve these issues, multicriteria decision-making (MCDM) analysis techniques were proposed as effective recommended solutions in the methodological aspect. This methodological aspect involves a proposed decision support system based on MCDM for evaluation and benchmarking to select suitable multiclass classification models for acute leukaemia. The said support system is examined and has three sequential phases. Phase One presents the identification procedure and process for establishing a decision matrix based on a crossover of evaluation criteria and acute leukaemia multiclass classification models. Phase Two describes the decision matrix development for the selection of acute leukaemia classification models based on the integrated Best and worst method (BWM) and VIKOR. Phase Three entails the validation of the proposed system.
    Matched MeSH terms: Automation*
  10. Fulltext An Automated Summarization Assessment Algorithm for Identifying Summarizing Strategies
    Abdi A, Idris N, Alguliyev RM, Aliguliyev RM
    PLoS One, 2016;11(1):e0145809.
    PMID: 26735139 DOI: 10.1371/journal.pone.0145809
    Summarization is a process to select important information from a source text. Summarizing strategies are the core cognitive processes in summarization activity. Since summarization can be important as a tool to improve comprehension, it has attracted interest of teachers for teaching summary writing through direct instruction. To do this, they need to review and assess the students' summaries and these tasks are very time-consuming. Thus, a computer-assisted assessment can be used to help teachers to conduct this task more effectively.
    Matched MeSH terms: Automation
  11. Fulltext Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning
    Lee S, Abdullah A, Jhanjhi N, Kok S
    PeerJ Comput Sci, 2021;7:e350.
    PMID: 33817000 DOI: 10.7717/peerj-cs.350
    The Industrial Revolution 4.0 began with the breakthrough technological advances in 5G, and artificial intelligence has innovatively transformed the manufacturing industry from digitalization and automation to the new era of smart factories. A smart factory can do not only more than just produce products in a digital and automatic system, but also is able to optimize the production on its own by integrating production with process management, service distribution, and customized product requirement. A big challenge to the smart factory is to ensure that its network security can counteract with any cyber attacks such as botnet and Distributed Denial of Service, They are recognized to cause serious interruption in production, and consequently economic losses for company producers. Among many security solutions, botnet detection using honeypot has shown to be effective in some investigation studies. It is a method of detecting botnet attackers by intentionally creating a resource within the network with the purpose of closely monitoring and acquiring botnet attacking behaviors. For the first time, a proposed model of botnet detection was experimented by combing honeypot with machine learning to classify botnet attacks. A mimicking smart factory environment was created on IoT device hardware configuration. Experimental results showed that the model performance gave a high accuracy of above 96%, with very fast time taken of just 0.1 ms and false positive rate at 0.24127 using random forest algorithm with Weka machine learning program. Hence, the honeypot combined machine learning model in this study was proved to be highly feasible to apply in the security network of smart factory to detect botnet attacks.
    Matched MeSH terms: Automation
  12. Fulltext Applications of Additively Manufactured Tools in Abrasive Machining-A Literature Review
    Deja M, Zieliński D, Kadir AZA, Humaira SN
    Materials (Basel), 2021 Mar 09;14(5).
    PMID: 33803424 DOI: 10.3390/ma14051318
    High requirements imposed by the competitive industrial environment determine the development directions of applied manufacturing methods. 3D printing technology, also known as additive manufacturing (AM), currently being one of the most dynamically developing production methods, is increasingly used in many different areas of industry. Nowadays, apart from the possibility of making prototypes of future products, AM is also used to produce fully functional machine parts, which is known as Rapid Manufacturing and also Rapid Tooling. Rapid Manufacturing refers to the ability of the software automation to rapidly accelerate the manufacturing process, while Rapid Tooling means that a tool is involved in order to accelerate the process. Abrasive processes are widely used in many industries, especially for machining hard and brittle materials such as advanced ceramics. This paper presents a review on advances and trends in contemporary abrasive machining related to the application of innovative 3D printed abrasive tools. Examples of abrasive tools made with the use of currently leading AM methods and their impact on the obtained machining results were indicated. The analyzed research works indicate the great potential and usefulness of the new constructions of the abrasive tools made by incremental technologies. Furthermore, the potential and limitations of currently used 3D printed abrasive tools, as well as the directions of their further development are indicated.
    Matched MeSH terms: Automation
  13. Fulltext Pseudorandom sequence contention algorithm for IEEE 802.11ah based internet of things network
    Raouf MA, Hashim F, Liew JT, Alezabi KA
    PLoS One, 2020;15(8):e0237386.
    PMID: 32790697 DOI: 10.1371/journal.pone.0237386
    The IEEE 802.11ah standard relies on the conventional distributed coordination function (DCF) as a backoff selection method. The DCF is utilized in the contention-based period of the newly introduced medium access control (MAC) mechanism, namely restricted access window (RAW). Despite various advantages of RAW, DCF still utilizes the legacy binary exponential backoff (BEB) algorithm, which suffers from a crucial disadvantage of being prone to high probability of collisions with high number of contending stations. To mitigate this issue, this paper investigates the possibility of replacing the existing exponential sequence (i.e., as in BEB) with a better pseudorandom sequence of integers. In particular, a new backoff algorithm, namely Pseudorandom Sequence Contention Algorithm (PRSCA) is proposed to update the CW size and minimize the collision probability. In addition, the proposed PRSCA incorporates a different approach of CW freezing mechanism and backoff stage reset process. An analytical model is derived for the proposed PRSCA and presented through a discrete 2-D Markov chain model. Performance evaluation demonstrates the efficiency of the proposed PRSCA in reducing collision probability and improving saturation throughput, network throughput, and access delay performance.
    Matched MeSH terms: Automation
  14. Fulltext A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
    Devan PAM, Hussin FA, Ibrahim R, Bingi K, Khanday FA
    Sensors (Basel), 2021 Jul 21;21(15).
    PMID: 34372210 DOI: 10.3390/s21154951
    Industrialization has led to a huge demand for a network control system to monitor and control multi-loop processes with high effectiveness. Due to these advancements, new industrial wireless sensor network (IWSN) standards such as ZigBee, WirelessHART, ISA 100.11a wireless, and Wireless network for Industrial Automation-Process Automation (WIA-PA) have begun to emerge based on their wired conventional structure with additional developments. This advancement improved flexibility, scalability, needed fewer cables, reduced the network installation and commissioning time, increased productivity, and reduced maintenance costs compared to wired networks. On the other hand, using IWSNs for process control comes with the critical challenge of handling stochastic network delays, packet drop, and external noises which are capable of degrading the controller performance. Thus, this paper presents a detailed study focusing only on the adoption of WirelessHART in simulations and real-time applications for industrial process monitoring and control with its crucial challenges and design requirements.
    Matched MeSH terms: Automation
  15. Fulltext Modified multi-class classification using association rule mining
    Yuhanis Yusof, Mohammed Hayel Refai
    Pertanika Journal of Science & Technology, 2013;21(1):205-216.
    MyJurnal
    As the amount of document increases, automation of classification that aids the analysis and management of documents receive focal attention. Classification, based on association rules that are generated from a collection of documents, is a recent data mining approach that integrates association rule mining and classification. The existing approaches produces either high accuracy with large number of rules or a small number of association rules that generate low accuracy. This work presents an association rule mining that employs a new item production algorithm that generates a small number of rules and produces an acceptable accuracy rate. The proposed method is evaluated on UCI datasets and measured based on prediction accuracy and the number of generated association rules. Comparison is later made against an existing classifier, Multi-class Classification based on Association Rule (MCAR). From the undertaken experiments, it is learned that the proposed method produces similar accuracy rate as MCAR but yet uses lesser number of rules.
    Matched MeSH terms: Automation
  16. Fulltext Introduction to robotics agriculture in pest control: a review
    Hassan, Ahmed, Abdul Shukor Juraimi, Muhammad Saiful Ahmad Hamdani
    The Pertanika Journal of Scholarly Research Reviews, 2016;2(2):80-93.
    MyJurnal
    Agriculture is one of the latest industries that uses robotic technologies. Cultivation of crops
    with high yield and quality can be enhanced when technological sustenance is involved. Pests are
    nuisance and cannot be completely eliminated, but with effective control and management. damages
    caused by pests could be minimized below economic threshold. Automation in agriculture is stable and
    accurate and is mainly incorporated in mechanized farming system. However its numerous application in
    different agricultural practices is not well noticed. Hence this paper attempts to provide profound
    awareness on robotic technology in agriculture. Robots could have a specific or multiple functions and,
    most commonly, they are made up of five basic components; sensors, effectors, actuators, controller and
    arms. Use of automation in weeding, weed mapping, micro spraying, seeding, irrigation and harvesting
    are progressions which promote sustainable agriculture and food security. In future, solar robots with
    battery inverter may be invented.
    Matched MeSH terms: Automation
  17. Fulltext Intelligent Monitoring interfaces for Coal fired Power Plant Boiler Trips: a review
    Nistah, N. N. M., Samyudia, Y., Alnaimi, F. B. I., Motalebi, F.
    Pertanika Journal of Science & Technology, 2014;22(2):593-601.
    MyJurnal
    A major source of contemporary power is a Coal-fired Power Plant. These power plants have the capacity to continuously supply electricity to almost 500,000 residential and business units. An essential component of a Coal-fired Power plant is automation. A feature of this automation is an Intelligent System developed for the Power Plant. These Intelligent Systems have different configurations and design. This research studies the various Intelligent Monitoring Interfaces developed for Coal-fired Power Plant Trips, their advantages, disadvantages and proposes a new Intelligent Monitoring Interface that would alleviate the disadvantages of the existing systems. Current systems that use Neural Network models are investigated. The improved Intelligent Monitoring Interface as proposed in this paper is a modification of the existing monitoring system for the Coal-fired Power Plant Boiler Trips. It is expected to improve the overall system by implementing remote accessibility and interactability between the plant operator and the control system interface. The interface will also assist the operator by providing guidelines to troubleshoot the identified trips and the remote server application will allow data collected to be viewed anytime, anywhere.
    Matched MeSH terms: Automation
  18. Fulltext An automated 3D modeling pipeline for constructing 3D models of MONOGENEAN HARDPART using machine learning techniques
    Teo BG, Dhillon SK
    BMC Bioinformatics, 2019 Dec 24;20(Suppl 19):658.
    PMID: 31870297 DOI: 10.1186/s12859-019-3210-x
    BACKGROUND: Studying structural and functional morphology of small organisms such as monogenean, is difficult due to the lack of visualization in three dimensions. One possible way to resolve this visualization issue is to create digital 3D models which may aid researchers in studying morphology and function of the monogenean. However, the development of 3D models is a tedious procedure as one will have to repeat an entire complicated modelling process for every new target 3D shape using a comprehensive 3D modelling software. This study was designed to develop an alternative 3D modelling approach to build 3D models of monogenean anchors, which can be used to understand these morphological structures in three dimensions. This alternative 3D modelling approach is aimed to avoid repeating the tedious modelling procedure for every single target 3D model from scratch.

    RESULT: An automated 3D modeling pipeline empowered by an Artificial Neural Network (ANN) was developed. This automated 3D modelling pipeline enables automated deformation of a generic 3D model of monogenean anchor into another target 3D anchor. The 3D modelling pipeline empowered by ANN has managed to automate the generation of the 8 target 3D models (representing 8 species: Dactylogyrus primaries, Pellucidhaptor merus, Dactylogyrus falcatus, Dactylogyrus vastator, Dactylogyrus pterocleidus, Dactylogyrus falciunguis, Chauhanellus auriculatum and Chauhanellus caelatus) of monogenean anchor from the respective 2D illustrations input without repeating the tedious modelling procedure.

    CONCLUSIONS: Despite some constraints and limitation, the automated 3D modelling pipeline developed in this study has demonstrated a working idea of application of machine learning approach in a 3D modelling work. This study has not only developed an automated 3D modelling pipeline but also has demonstrated a cross-disciplinary research design that integrates machine learning into a specific domain of study such as 3D modelling of the biological structures.

    Matched MeSH terms: Automation, Laboratory
  19. Fulltext Retrofitting ancient timber glulam mortise & tenon construction joints through computer-aided laser cutting
    Zhan Z, Wang C, Yap JBH, Loi MS
    Heliyon, 2020 Apr;6(4):e03671.
    PMID: 32382668 DOI: 10.1016/j.heliyon.2020.e03671
    This study is aimed to rationalise and demonstrate the efficacy of utilising laser cutting technique in the fabrication of glulam mortise & tenon joints in timber frame. Trial-and-error experiments aided by laser cutter were conducted to produce 3D timber mortise & tenon joints models. The two main instruments used were 3D modelling software and the laser cutter TH 1390/6090. Plywood was chosen because it could produce smooth and accurate cut edges whereby the surface could remain crack-free, and it could increase stability due to its laminated nature. Google SketchUp was used for modelling and Laser CAD v7.52 was used to transfer the 3D models to the laser cutter because it is compatible with AI, BMP, PLT, DXF and DST templates. Four models were designed and fabricated in which the trial-and-error experiments proved laser cutting could speed up the manufacturing process with superb quality and high uniformity. Precision laser cutting supports easy automation, produces small heat-affected zone, minimises deformity, relatively quiet and produces low amount of waste. The LaserCAD could not process 3D images directly but needed 2D images to be transferred, so layering and unfolding works were therefore needed. This study revealed a significant potential of rapid manufacturability of mortise & tenon joints with high-quality and high-uniformity through computer-aided laser cutting technique for wide applications in the built environment.
    Matched MeSH terms: Automation
  20. Smart systems in producing algae-based protein to improve functional food ingredients industries
    Neo YT, Chia WY, Lim SS, Ngan CL, Kurniawan TA, Chew KW
    Food Res Int, 2023 Mar;165:112480.
    PMID: 36869493 DOI: 10.1016/j.foodres.2023.112480
    Production and extraction systems of algal protein and handling process of functional food ingredients need to control several parameters such as temperature, pH, intensity, and turbidity. Many researchers have investigated the Internet of Things (IoT) approach for enhancing the yield of microalgae biomass and machine learning for identifying and classifying microalgae. However, there have been few specific studies on using IoT and artificial intelligence (AI) for production and extraction of algal protein as well as functional food ingredients processing. In order to improve the production of algal protein and functional food ingredients, the implementation of smart system is a must to have real-time monitoring, remote control system, quick response to sudden events, prediction and characterisation. Techniques of IoT and AI are expected to help functional food industries to have a big breakthrough in the future. Manufacturing and implementation of beneficial smart systems are important to provide convenience and to increase the efficiency of work by using the interconnectivity of IoT devices to have good capturing, processing, archiving, analyzing, and automation. This review investigates the possibilities of implementation of IoT and AI in production and extraction of algal protein and processing of functional food ingredients.
    Matched MeSH terms: Automation
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