• Advances in Energy Research
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• v.8 no.2
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• pp.95-110
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• 2022

Wind energy can be utilized for the generation of electricity, due to significant wind potential at different parts of the world, some countries have already been generating of electricity through wind. Pakistan is still well behind and has not yet made any appreciable effort for the same. The objective of this work was to add some new strategies to calculate Weibull parameters and assess wind energy potential. A new approach calculates Weibull parameters; we also developed an alternate formula to calculate shape parameters instead of the gamma function. We obtained k (shape parameter) and c (scale parameter) for two-parameter Weibull distribution using five statistical methods for five different cities in Pakistan. Maximum likelihood method, Modified Maximum likelihood Method, Method of Moment, Energy Pattern Method, Empirical Method, and have been to calculate and differentiate the values of (shape parameter) k and (scale parameter) c. The performance of these five methods is estimated using the Goodness-of-Fit Test, including root mean square error, mean absolute bias error, mean absolute percentage error, and chi-square error. The daily 10-minute average values of wind speed data (obtained from energydata.info) of different cities of Pakistan for the year 2016 are used to estimate the Weibull parameters. The study finds that Hyderabad city has the largest wind potential than Karachi, Quetta, Lahore, and Peshawar. Hyderabad and Karachi are two possible sites where wind turbines can produce reasonable electricity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.107-109
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• 2022

This paper proposes a new security system to detect cyber-attacks based on message authentication in a in-vehicle network. In the in-vehicle network, when a sending node transmits messages in a broadcast manner, it only uses a message identifier, rather than a node's identifier. It leads to a problem not identifying the source. In the proposed system, the sending node generates a message authentication code (MAC) using a cryptographic hash function to the control data and transmits it with the control data. When generating the MAC for each message, a multidimensional chaotic map is applied to increase the randomness of the result. The receiving node compares its MAC generated from the control data in the received message with the MAC of the received message to detect whether the message transmitted from the sending node is forged or not. We evaluate the performance of the proposed system by using CANoe and CAPL (Communication Access Programming Language). Our system shows a 100% of detection rate against cyber-attacks injected.

• Advances in nano research
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• v.15 no.5
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• pp.385-399
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• 2023

Hexagonal boron nitride (h-BN), commonly referred to as Boron Nitride Nanoribbons (BNNRs), is an electrical insulator characterized by high thermal stability and a wide bandgap semiconductor property. This study delves into the electronic properties of two BNNR configurations: Armchair BNNRs (ABNNRs) and Zigzag BNNRs (ZBNNRs). Utilizing the nearest-neighbour tight-binding approach and numerical methods, the electronic properties of BNNRs were simulated. A simplifying assumption, the Hamiltonian matrix is used to compute the electronic properties by considering the self-interaction energy of a unit cell and the interaction energy between the unit cells. The edge perturbation is applied to the selected atoms of ABNNRs and ZBNNRs to simulate the electronic properties changes. This simulation work is done by generating a custom script using numerical computational methods in MATLAB software. When benchmarked against a reference study, our results aligned closely in terms of band structure and bandgap energy for ABNNRs. However, variations were observed in the peak values of the continuous curves for the local density of states. This discrepancy can be attributed to the use of numerical methods in our study, in contrast to the semi-analytical approach adopted in the reference work.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.267-275
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• 2023

This paper presents the development of specialized software for annotating volume-of-interest on ¹⁸F-FDG PET/CT images with the goal of facilitating the studies and diagnosis of head and neck cancer (HNC). To achieve an efficient annotation process, we employed the SE-Norm-Residual Layer-based U-Net model. This model exhibited outstanding proficiency to segment cancerous regions within ¹⁸F-FDG PET/CT scans of HNC cases. Manual annotation function was also integrated, allowing researchers and clinicians to validate and refine annotations based on dataset characteristics. Workspace has a display with fusion of both PET and CT images, providing enhance user convenience through simultaneous visualization. The performance of deeplearning model was validated using a Hecktor 2021 dataset, and subsequently developed semi-automatic annotation functionalities. We began by performing image preprocessing including resampling, normalization, and co-registration, followed by an evaluation of the deep learning model performance. This model was integrated into the software, serving as an initial automatic segmentation step. Users can manually refine pre-segmented regions to correct false positives and false negatives. Annotation images are subsequently saved along with their corresponding ¹⁸F-FDG PET/CT fusion images, enabling their application across various domains. In this study, we developed a semi-automatic annotation software designed for efficiently generating annotated lesion images, with applications in HNC research and diagnosis. The findings indicated that this software surpasses conventional tools, particularly in the context of HNC-specific annotation with ¹⁸F-FDG PET/CT data. Consequently, developed software offers a robust solution for producing annotated datasets, driving advances in the studies and diagnosis of HNC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.368-374
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• 2024

During an X-ray examination, the beam of radiation is dispersed in many directions. We believe that managing radiation dose is about providing transparency to users and patients in the accurate investigation and analysis of radiation dose. The purpose of measuring the radiation dose as a function of location is to ensure that medical personnel using the equipment or participating in the operating room are minimally harmed by the different radiation doses depending on their location. Four mobile diagnostic X-ray units were used to analyze the radiation dose depending on the spatial location. The image intensifier and the flat panel detector type that receives the image analyzed the dose by angle to measure the distribution of the exposure dose by location. The radiation equipment used was composed of four units, and measuring devices were installed according to the location. The X-ray (C-arm) was measured by varying the position from 0 to 360 degrees, and the highest dose was measured at the center position based on the abdominal position, and the highest dose was measured at the 90° position for the head position when using the image intensifier equipment. The operator or medical staff can see that the radiation dose varies depending on the position of the diagnostic radiation generator. In the image intensifier and flat panel detector type that accepts images, the dose by angle was analyzed for the distribution of exposed dose by position, and the measurement method should be changed according to the provision of dose information that is different from the dose output from the equipment according to the position.

• International Journal of Stem Cells
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• v.16 no.1
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• pp.108-116
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• 2023

γδ T cells are a rare and unique prototype of T cells that share properties with natural killer cells in secondary lymphoid organs. Although many studies have revealed the function and importance of adult-derived γδ T cells in cancer biology and regenerative medicine, the low numbers of these cells hamper their application as therapeutic cell sources in the clinic. To solve this problem, pluripotent stem cell-derived γδ T cells are considered alternative cell sources; however, few studies have reported the generation of human pluripotent stem cell-derived γδ T cells. In the present study, we investigated whether lymphoid lineage γδ T cells were successfully generated from human pluripotent stem cells via hemogenic endothelium under defined culture conditions. Our results revealed that pluripotent stem cells successfully generated γδ T cells with an overall increase in transcriptional activity of lymphoid lineage genes and cytolytic factors, indicating the importance of the optimization of culture conditions in generating lymphoid lineage γδ T cells. We uncovered an initial step in differentiating γδ T cells that could be applied to basic and translational investigations in the field of cancer biology. Based on our result, we will develop an appropriate method to purify γδ T cells with functionality and it helpful for the study of basic mechanism of γδ T cells in pathophysiologic condition as well as clinic application.

• Journal of the Korea Society for Simulation
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• v.33 no.1
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• pp.19-26
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• 2024

The phase-type, PH, distribution is defined as the time to absorption into a terminal state in a continuous-time Markov chain. As the PH distribution includes family of exponential distributions, it has been widely used in stochastic models. Since the PH distribution is represented and generated by an initial probability vector and a generator matrix which is called the Markovian representation, we need to find a vector and a matrix that are consistent with given set of moments if we want simulate a PH distribution. In this paper, we propose an approach to simulate a PH distribution based on distribution function which can be obtained directly from moments. For the simulation of PH distribution of order 2, closed-form formula and streamlined procedures are given based on the Jordan decomposition and the minimal Laplace transform which is computationally more efficient than the moment matching methods for the Markovian representation. Our approach can be used more effectively than the Markovian representation in generating higher order PH distribution in queueing network simulation.

• Convergence Security Journal
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• v.23 no.4
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• pp.93-100
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• 2023

This paper presents a novel approach for assigning group keys within a dynamic swarm unmanned system environment. In this environment, multiple groups of unmanned systems have the flexibility to merge into a single group or a single unmanned system group can be subdivided into multiple groups. The proposed protocol encompasses two key steps: group key generation and sharing. The responsibility of generating the group key rests solely with the leader node of the group. The group's leader node employs a secret sharing scheme to fragment the group key into multiple fragments, which are subsequently transmitted. Nodes that receive these fragments reconstruct a fresh group key by combining their self-generated secret fragment with the fragment obtained from the leader node. Subsequently, they validate the integrity of the derived group key by employing the hash function. The efficacy of the proposed technique is ascertained through an exhaustive assessment of its security and communication efficiency. This analysis affirms its potential for robust application in forthcoming swarm unmanned system operations scenarios characterized by frequent network group modifications.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.1-12
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• 2008

In this paper, we present a real-time algorithm for recognizing the vehicle color from the indoor and outdoor vehicle images based on GPU (Graphics Processing Unit) acceleration. In the preprocessing step, we construct feature victors from the sample vehicle images with different colors. Then, we combine the feature vectors for each color and store them as a reference texture that would be used in the GPU. Given an input vehicle image, the CPU constructs its feature Hector, and then the GPU compares it with the sample feature vectors in the reference texture. The similarities between the input feature vector and the sample feature vectors for each color are measured, and then the result is transferred to the CPU to recognize the vehicle color. The output colors are categorized into seven colors that include three achromatic colors: black, silver, and white and four chromatic colors: red, yellow, blue, and green. We construct feature vectors by using the histograms which consist of hue-saturation pairs and hue-intensity pairs. The weight factor is given to the saturation values. Our algorithm shows 94.67% of successful color recognition rate, by using a large number of sample images captured in various environments, by generating feature vectors that distinguish different colors, and by utilizing an appropriate likelihood function. We also accelerate the speed of color recognition by utilizing the parallel computation functionality in the GPU. In the experiments, we constructed a reference texture from 7,168 sample images, where 1,024 images were used for each color. The average time for generating a feature vector is 0.509ms for the $150{\times}113$ resolution image. After the feature vector is constructed, the execution time for GPU-based color recognition is 2.316ms in average, and this is 5.47 times faster than the case when the algorithm is executed in the CPU. Our experiments were limited to the vehicle images only, but our algorithm can be extended to the input images of the general objects.