• Journal of Power Electronics
• /
• v.19 no.3
• /
• pp.702-713
• /
• 2019

Multilevel Inverters (MLIs) are widely used in medium voltage applications due to their various advantages. In addition, there are numerous types of MLIs for such applications. However, the diode-less 3-level (3L) T-type Neutral Point Clamped (NPC) MLI is the most advantageous due to its low conduction losses and high potential efficiency. The power circuit of a 3L T-type NPC is derived by the conventional two level inverter by a slight modification. In order to explore the MLI performance for various Pulse Width Modulation (PWM) schemes, this paper examines the operation of a 3L (five level line to line) T-type NPC MLI for various types of Multi-Carriers Pulse Width Modulation (MCPWM) schemes. These PWM schemes are compared in terms of their voltage profile, total harmonic distortion (THD) and conduction losses. In addition, a 3L T-type NPC MLI is also compared with the conventional NPC in terms of number of switches, clamping diodes, main diodes and capacitors. Moreover, the capacitor-balancing problem is also investigated using the Neutral Point Fluctuation (NPF) method with all of the MCPWM schemes. A 1kW 3L T-type NPC MLI is simulated in MATLAB/Simulink and implemented experimentally and its performance is tested with a 1HP induction motor. The results indicate that the 3L T-type NPC MLI has better performance than conventional NPC MLIs.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3708-3728
• /
• 2021

Indian herbal plants are used in agriculture and in the food, cosmetics, and pharmaceutical industries. Laboratory-based tests are routinely used to identify and classify similar herb species by analyzing their internal cell structures. In this paper, we have applied computer vision techniques to do the same. The original leaf image was preprocessed using the Chan-Vese active contour segmentation algorithm to efface the background from the image by setting the contraction bias as (v) -1 and smoothing factor (µ) as 0.5, and bringing the initial contour close to the image boundary. Thereafter the segmented grayscale image was fed to a leaky capacitance fired neuron model (LCFN), which differentiates between similar herbs by combining different groups of pixels in the leaf image. The LFCN's decay constant (f), decay constant (g) and threshold (h) parameters were empirically assigned as 0.7, 0.6 and h=18 to generate the 1D feature vector. The LCFN time sequence identified the internal leaf structure at different iterations. Our proposed framework was tested against newly collected herbal species of natural images, geometrically variant images in terms of size, orientation and position. The 1D sequence and shape features of aloe, betel, Indian borage, bittergourd, grape, insulin herb, guava, mango, nilavembu, nithiyakalyani, sweet basil and pomegranate were fed into the 5-fold Bayesian regularization neural network (BRNN), K-nearest neighbors (KNN), support vector machine (SVM), and ensemble classifier to obtain the highest classification accuracy of 91.19%.

• Geomechanics and Engineering
• /
• v.30 no.3
• /
• pp.281-291
• /
• 2022

Complex rock masses include various joint planes, bedding planes and other weak structural planes. The existence of these structural planes affects the mechanical properties, deformation rules and failure modes of jointed rock masses. To study the influence of the parameters of a nonpersistent joint network on the mechanical properties and failure modes of jointed rock masses, synthetic rock mass (SRM) technology based on discrete elements is introduced. The results show that as the size of the joints in the rock mass increases, the compressive strength and the discreteness of the rock mass first increase and then decrease. Among them, the joints that are characterized by "small but many" joints and "large and clustered" joints have the most significant impact on the strength of the rock mass. With the increase in joint density in the rock mass, the compressive strength of rock mass decreases monotonically, but the rate of decrease gradually decreases. With the increase in the joint dip angle in rock mass, the strength of the rock mass first decreases and then increases, forming a U-shaped change rule. In the analysis of the failure mode and deformation of a jointed rock mass, the type of plastic zone formed after rock mass failure is closely related to the macroscopic displacement deformation of the rock mass and the parameters of the joints, which generally shows that the location and density of the joints greatly affect the failure mode and displacement degree of the jointed rock mass. The instability mechanism of jointed surrounding rock is revealed.

• Journal of the Korean Society of Food Culture
• /
• v.37 no.6
• /
• pp.540-546
• /
• 2022

This study aimed to determine the changes in the vitamin B₅ content of raw and cooked vegetables. The nineteen vegetables were subjected to different cooking methods, viz. blanching, boiling, pan-broiling, and steaming. Vitamin B₅ was quantified by reversed-phase high-performance liquid chromatography (HPLC) using photodiode-array (PDA) detection (200 nm). The standard reference materials (SRM) were used to validate the accuracy of vitamin B₅ measurement method used in this study. The cooking yields ranged from 82.63 to 107.62% and decreased in most of the vegetables except bitter melon, curled mallow, and eggplant. The raw kabocha squash, Danhobak, had the highest vitamin B₅ content (0.671 mg/100 g) among the samples. All cooked vegetables showed lower vitamin B₅ content compared to the raw samples. The true retention ranged from 0% (crown daisy, blanching) to 84.49% (kabocha squash, steaming). These results indicate that vitamin B₅ is degraded after cooking. Pan-broiling and steaming are better cooking methods than the others for retaining vitamin B₅. The true retention of vitamin B₅ in the samples markedly depends on the cooking method and food matrix. These results can be used as important basic data for nutritional evaluation of meals.

• Journal of the Korea Society for Simulation
• /
• v.19 no.4
• /
• pp.249-259
• /
• 2010

Synthetic environment data used in defense M&S fields, which came from various organization and source, are consumed and managed by their own native database system in distributed environment. But to manage these diverse data while interoperation in HLA/RTI environment, neutral synthetic environment data model is necessary to transmit the data between native database. By the support of DMSO, SEDRIS was developed to achieve this requirement and this specification guarantees loss-less data representation, interchange and interoperability. In this research, to use SEDRIS as a standard simulation database, base research, visualization for validation, data interchange experiment through test-bed was done. This paper shows each research case, result and future research direction, to propose standardized SEDRIS usage process.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.6
• /
• pp.1706-1727
• /
• 2023

Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

• Geomechanics and Engineering
• /
• v.31 no.2
• /
• pp.129-147
• /
• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Journal of Integrative Natural Science
• /
• v.16 no.4
• /
• pp.123-131
• /
• 2023

Breast cancer continues to pose a substantial worldwide health challenge, thereby requiring the development of innovative strategies to discover new therapeutic interventions. Signal Transducer and Activator of Transcription 3 (STAT-3) has been identified as a significant factor in the development of several types of cancer, including breast cancer. This is primarily attributed to its diverse functions in promoting tumour formation and conferring resistance to therapeutic interventions. This study presents an in silico drug repositioning approach that focuses on identifying specific inhibitors of STAT-3 for the purpose of treating breast cancer. We initially examined the structural and functional attributes of STAT-3, thereby elucidating its crucial involvement in cellular signalling cascades. A comprehensive virtual screening was performed on a diverse collection of drugs that have been approved by the FDA from zinc15 database. Various computational techniques, including molecular docking, cross docking, and cDFT analysis, were utilised in order to prioritise potential candidates. This prioritisation was based on their predicted binding energies and outer molecular orbital reactivity. The findings of our study have unveiled a Dihydroergotamine and Paritaprevir that have been approved by the FDA and exhibit considerable promise as selective inhibitors of STAT-3. In conclusion, the utilisation of our in silico drug repositioning approach presents a prompt and economically efficient method for the identification of potential compounds that warrant subsequent experimental validation as selective STAT-3 inhibitors in the context of breast cancer. The present study highlights the considerable potential of employing computational strategies to expedite the drug discovery process. Moreover, it provides valuable insights into novel avenues for targeted therapeutic interventions in the context of breast cancer treatment.

• International Journal of Computer Science & Network Security
• /
• v.24 no.9
• /
• pp.63-76
• /
• 2024

Weather forecasting has become a very popular topic nowadays among researchers because of its various effects on global lives. It is a technique to predict the future, what is going to happen in the atmosphere by analyzing various available datasets such as rain, snow, cloud cover, temperature, moisture in the air, and wind speed with the help of our gained scientific knowledge i.e., several approaches and set of rules or we can say them as algorithms that are being used to analyze and predict the weather. Weather analysis and prediction are required to prevent nature from natural losses before it happens by using a Deep Learning Approach. This analysis and prediction are the most challenging task because of having multidimensional and nonlinear data. Several Deep Learning Approaches are available: Numerical Weather Prediction (NWP), needs a highly calculative mathematical equation to gain the present condition of the weather. Quantitative precipitation nowcasting (QPN), is also used for weather prediction. In this article, we have implemented and analyzed the various distinct techniques that are being used in data mining for weather prediction.

• Journal of Food Hygiene and Safety
• /
• v.31 no.5
• /
• pp.327-334
• /
• 2016

This study was conducted to establish the standard method for the contents of biotin in milk formulas. To optimize the method, we compared several conditions for liquid extraction, purification and instrumental measurement using spiked samples and certified reference material (NIST SRM 1849a) as test materials. LC-MS/MS method for biotin was established using $C_{18}$ column and binary gradient 0.1% formic acid/acetonitrile, 0.1% formic acid/water mobile phase is applied for biotin. Product-ion traces at m/z 245.1 ${\rightarrow}$ 227.1, 166.1 are used for quantitative analysis of biotin. The linearity was over $R^2=0.999$ in range of $5{\sim}60{\mu}g/L$. For purification, chloroform was used as a solvent for eliminating lipids in milk formula. The linearity was over 0.999 in range of 5~60 ng/mL. The detection limit and quantification limit were 0.10, 0.31 ng/mL. The accuracy and precision of LC-MS/MS method using CRM were 103%, 2.5% respectively. Optimized methods were applied in sample analysis to verify the reliability. All the tested milk formulas were acceptable contents of biotin compared with component specification and standards for nutrition labeling. The standard operating procedures were prepared for biotin to provide experimental information and to strengthen the management of nutrient in milk formula.