Enhanced Job Ranking Backfilling Based on Linear and Logarithmic Ranking Equations

Grid system is used by many researchers’ and scholars all over the world to solve the complicated and complex problems in different sciences. Job ranking backfilling is the most used model by many researchers in grid system to improve the performance of job scheduling algorithm. The model aims on serving the smallest job in the queue. As a second improvement of job backfilling, researchers proposed job ranking backfilling that serve job based on ranking equation. This paper proposes an enhance job ranking algorithm based on using linear and logarithmic ranking equations. Both proposed ranking equations used curve estimation model to predict on the variables’ coefficients. By simulation and after different tests, the average results of job ranking backfilling with linear ranking equation outperform conventional job ranking backfilling with improvement equal 3.2% and 56.53% in total execution time and average waiting time, respectively. In addition, job ranking backfilling with logarithmic ranking equation shows average improvement equal 1.78% and 46.62% in total execution time and average waiting time, respectively. The results indicate that the proposed ranking equations would improve conventional job ranking backfilling in high and low demand grid system under different condition

Rekabentuk Sistem Pengecaman Puncak Isyarat Elektrokardiogram Menggunakan Rangkaian Hmlp Berbilang Untuk Mendiagnosis Kecacatan Jantung

EKG adalah satu sistem analisa yang digunakan oleh pakar kardiologi untuk mentafsir status jantung yang murah, efektif, mudah diimplementasi dan selamat digunakan. ECG is a heart status analysis system used by cardiologist to interprets which is cheap, effective, easy for implementation and safe to be used

Objective functions modification of GA optimized PID controller for brushed DC motor

PID Optimization by Genetic Algorithm or any intelligent optimization method is widely being used recently. The main issue is to select a suitable objective function based on error criteria. Original error criteria that is widely being used such as ITAE, ISE, ITSE and IAE is insufficient in enhancing some of the performance parameter. Parameter such as settling time, rise time, percentage of overshoot, and steady state error is included in the objective function. Weightage is added into these parameters based on users’ performance requirement. Based on the results, modified error criteria show improvement in all performance parameter after being modified. All of the error criteria produce 0% overshoot, 29.51%-39.44% shorter rise time, 21.11%-42.98% better settling time, 10% to 53.76% reduction in steady state error. The performance of modified objective function in minimizing the error signal is reduced. It can be concluded that modification of objective function by adding performance parameter into consideration could improve the performance of rise time, settling time, overshoot percentage, and steady state erro

Solving Vehicle Routing Problem using Ant Colony Optimisation (ACO) Algorithm

Engineering field usually requires having the best design for an optimum performance, thus optimization plays an important part in this field. The vehicle routing problem (VRP) has been an important problem in the field of distribution and logistics since at least the early 1960s. Hence, this study was about the application of ant colony optimization (ACO) algorithm to solve vehicle routing problem (VRP). Firstly, this study constructed the model of the problem to be solved through this research. The study was then focused on the Ant Colony Optimization (ACO). The objective function of the algorithm was studied and applied to VRP. The effectiveness of the algorithm was increased with the minimization of stopping criteria. The control parameters were studied to find the best value for each control parameter. After the control parameters were identified, the evaluation of the performance of ACO on VRP was made. The good performance of the algorithm reflected on the importance of its parameters, which were number of ants (nAnt), alpha (α), beta (β) and rho (ρ). Alpha represents the relative importance of trail, beta represents the importance of visibility and rho represents the parameter governing pheromone decay. The route results of different iterations were compared and analyzed the performance of the algorithm. The best set of control parameters obtained is with 20 ants, α = 1, β = 1 and ρ = 0.05. The average cost and standard deviation from the 20 runtimes with best set of control parameters were also evaluated, with 1057.839 km and 25.913 km respectively. Last but not least, a conclusion is made to summarize the achievement of the study

Time-aware Traffic Shaper using Time-based Packet Scheduling on Intel I210

By 2015, the Institute of Electrical and Electronics Engineers (IEEE) time-sensitive networking (TSN) task group has released several TSN standards. Amongst them is 802.1Qbv, also known as a time-aware shaper, aiming to provide performance assurances of latency and delivery variation to enable applications in a TSN network. While there are several products and evaluation kits that employ 802.1Qbv in the market now, it is still not widely adopted yet due to the maturity of the standard. Hardware-enabled 802.1Qbv use hardware queues and timers to achieve accurate transmission of packets in the switch and bridge. This research aims to investigate the feasibility of using an existing end-station Ethernet controller, Intel I210, and its launch time control feature (commonly known as time-based packet scheduling) to shape traffic compatible to 802.1Qbv-enabled network bridges. A software solution is developed by implementing a software configurable gate-control list and employing open-source Linux RFC patches for per-packet transmit time specification. By configuring the kernel and mapping kernel-layer traffic classes to the hardware queues, packets can be transmitted out at precise times while attaching 802.1Q VLAN tags, required by bridges to identify packets. Through analysis, it is found that this solution will require an additional 30 μs transmit offset to be used effectively. That is 55% more time is needed to transmit a packet in a back-to-back connection and 17.6% on a 3-switch network to improve period peak jitter performance to just 8.9  μs compared to 1 ms on solutions that send packets out periodically using software sleep functions

Leveraging on Advanced Remote Sensing- and Artificial Intelligence-Based Technologies to Manage Palm Oil Plantation for Current Global Scenario: A Review

Advanced remote sensing technologies have undoubtedly revolutionized palm oil industry management by bringing business and environmental benefits on a single platform. It is evident from the ongoing trend that remote sensing using satellite and aerial data is able to provide precise and quick information for huge palm oil plantation areas using high-resolution image processing, which is also recognized by the certification agencies, i.e., the Roundtable on Sustainable Palm Oil (RSPO) and ISCC (International Sustainability and Carbon Certification). A substantial improvement in the palm oil industry could be attained by utilizing the latest Geo-information tools and technologies equipped with AI (Artificial Intelligence) algorithms and image processing, which could help to identify illegal deforestation, tree count, tree height, and the early detection of diseased leaves. This paper reviews some of the latest technologies equipped with remote sensing, AI, and image processing for managing the palm oil plantation. This manuscript also highlights how the distress in the current palm oil industry could be handled by mentioning some of the improvised monitoring systems for palm oil plantation that could in turn increase the yield of palm oil. It is evident from the proposed review that the accuracy of AI algorithms for palm oil detection depends on various factors such as the quality of the training data, the design of the neural network, and the type of detection task. In general, AI models have achieved high accuracy in detecting palm oil tree images, with some studies reporting accuracy levels up to 91%. However, it is important to note that accuracy can still be affected by factors such as variations in lighting conditions and image resolution. Nonetheless, with any AI model, the accuracy of algorithms for palm oil tree detection can be improved by collecting more diverse training data and fine-tuning the model

Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia

The impact of floods is the most severe among the natural calamities occurring in Malaysia. The knock of floods is consistent and annually forces thousands of Malaysians to relocate. The lack of information from the Ministry of Environment and Water, Malaysia is the foremost obstacle in upgrading the flood mapping. With the expeditious evolution of computer techniques, processing of satellite and unmanned aerial vehicle (UAV) images for river hydromorphological feature detection and flood management have gathered pace in the last two decades. Different image processing algorithms—structure from motion (SfM), multi-view stereo (MVS), gradient vector flow (GVF) snake algorithm, etc.—and artificial neural networks are implemented for the monitoring and classification of river features. This paper presents the application of the k-means algorithm along with image thresholding to quantify variation in river surface flow areas and vegetation growth along Kerian River, Malaysia. The river characteristic recognition directly or indirectly assists in studying river behavior and flood monitoring. Dice similarity coefficient and Jaccard index are numerated between thresholded images that are clustered using the k-means algorithm and manually segmented images. Based on quantitative evaluation, a dice similarity coefficient and Jaccard index of up to 97.86% and 94.36% were yielded for flow area and vegetation calculation. Thus, the present technique is functional in evaluating river characteristics with reduced errors. With minimum errors, the present technique can be utilized for quantifying agricultural areas and urban areas around the river basin