• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1276-1295
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• 2023

Sensor networks are now an essential aspect of wireless communication, especially with the introduction of new gadgets and protocols. Their ability to be deployed anywhere, especially where human presence is undesirable, makes them perfect choices for remote observation and control. Despite their vast range of applications from home to hostile territory monitoring, limited battery power remains a limiting factor in their efficacy. To analyze and transmit data, it requires intelligent use of available battery power. Several studies have established effective routing algorithms based on clustering. However, choosing optimal cluster heads and similarity measures for clustering significantly increases computing time and cost. This work proposes and implements a simple two-phase technique of route creation and maintenance to ensure route reliability by employing nature-inspired ant colony optimization followed by the fuzzy decision engine (FDE). Benchmark methods such as PSO, ACO and GWO are compared with the proposed HRCM's performance. The objective has been focused towards establishing the superiority of proposed work amongst existing optimization methods in a standalone configuration. An average of 15% improvement in energy consumption followed by 12% improvement in latency reduction is observed in proposed hybrid model over standalone optimization methods.

• ETRI Journal
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• 제45권3호
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• pp.448-461
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• 2023

This article performs a detailed data scrutiny on a chronic kidney disease (CKD) dataset to select efficient instances and relevant features. Data relevancy is investigated using feature extraction, hybrid outlier detection, and handling of missing values. Data instances that do not influence the target are removed using data envelopment analysis to enable reduction of rows. Column reduction is achieved by ranking the attributes through feature selection methodologies, namely, extra-trees classifier, recursive feature elimination, chi-squared test, analysis of variance, and mutual information. These methodologies are ranked via Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using weight optimization to identify the optimal features for model building from the CKD dataset to facilitate better prediction while diagnosing the severity of the disease. An efficient hybrid ensemble and novel similarity-based classifiers are built using the pruned dataset, and the results are thereafter compared with random forest, AdaBoost, naive Bayes, k-nearest neighbors, and support vector machines. The hybrid ensemble classifier yields a better prediction accuracy of 98.31% for the features selected by extra tree classifier (ETC), which is ranked as the best by TOPSIS.

• Korean Journal of Microbiology
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• 제31권3호
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• pp.179-183
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• 1993

A restricted facultatively methanol-oxidizing bacterium, Methylovorus sp. strain SS1, was isolate dfrom soil samples from Kuala Lumpur, Malaysia, through methanol-enrichment culture technique. The isolate was nonmotile Gram-negative rod and did not have complex internal membrane system. The colonies were small, pale-yellow, and raised convex with entire margin. The cell did not produce any spores and capsular materials. The cell was obligately aerobic and exhibited catalase, but no oxidase, activity. Plasmid, carotenoid pigment, and poly-.betha.-hydroxybutyric acid were not found. The guanine plus cytosine content of the DNA was 55%. The isolate was found to grow only on methanol methylamine, or glucose. Growth factors were not required. Cells growing on methanol was found to produce extracellular polysaccharides containing glucose, lactose, and fructose. Growth was optimal (t$_{d}$= 1.7) with 0.5%(v/v) methanol at 40.deg.C and pH 6.5. No Growth was observed at over 60.deg.C. Cell-free extracts of the methanol grown cells exhibited the phenazine methosulfate-linked methanol dehydrogenase activity Methanol was found to be assimilate dthrough the ribulose monophosphate pathway.y.

• Journal of the Korean Society of Propulsion Engineers
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• 제27권1호
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• pp.17-26
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• 2023

The star-shaped propellant grain can be used for designing burning surface areas with various profiles and are easy to manufacture, so it can be usefully applied to actual solid rocket motors. However, since there are many design-related configuration variables and slivers at the end of combustion, it is difficult to achieve an optimal design using a general optimization technique. In this study, the new method for designing star grains using a database was proposed to increase usability and success rate of optimization design. In the proposed method, a solution that satisfies the requirements is obtained after defining the performance variables, constructing the database. By applying the proposed method, the design of star grains with various profiles of the burning surface area was performed, and the validity of the design method was confirmed.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.123-133
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• 2023

Active queue management (AQM) is a leading congestion control system, which can keep smaller queuing delay, less packet loss with better network utilization and throughput by intentionally dropping the packets at the intermediate hubs in TCP/IP (transmission control protocol/Internet protocol) networks. To accelerate the responsiveness of AQM framework, proportional-integral-differential (PID) controllers are utilized. In spite of its simplicity, it can effectively take care of a range of complex problems; however it is a lot complicated to track down optimal PID parameters with conventional procedures. A few new strategies have been grown as of late to adjust the PID controller parameters. Therefore, in this paper, we have developed a Squirrel search based PID controller to dynamically find its controller gain parameters for AQM. The controller gain parameters are decided based on minimizing the integrated-absolute error (IAE) in order to ensure less packet loss, high link utilization and a stable queue length in favor of TCP networks.

• The Journal of Asian Finance, Economics and Business
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• 제9권1호
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• pp.105-119
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• 2022

This study aims to develop a concept based on empirical research on improving optimal business performance. This goal is achieved by examining the relationship between variables of entrepreneurial orientation, talent management, market orientation, and business performance. The construction of the relationship between research variables, namely entrepreneurial orientation, talent management, on business performance is mediated by market orientation on handicraft businesses in Indonesia. The sampling method was used to collect data from 145 businessmen in Indonesia, using surveys and questionnaires. Data was collected using a survey technique carried out from June 2020 to December 2020. The data obtained was analyzed using the PLS Pro 19. This study developed 9 hypotheses that were tested directly, indirectly, and through mediation. This study has five findings. First, Entrepreneurship Orientation does not directly affect Business Performance. Second, Entrepreneurship Orientation also has a significant direct effect on Talent Management and market orientation. Third, Talent Management and market orientation have a direct and significant impact on business performance. Fourth, market orientation mediates the effect of entrepreneurial orientation on business performance. Fifth, talent management mediates the effect of entrepreneurial orientation on business performance. The results show that entrepreneurial orientation mediated by talent management and market orientation can improve creative industry business performance for the better.

• Journal of Information Processing Systems
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• 제18권6호
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• pp.813-821
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• 2022

Intelligent monitoring, life entertainment, medical rehabilitation, and other fields are only a few examples where visual image technology is becoming increasingly sophisticated and playing a significant role. Recognizing Wushu, or martial arts, movements through the use of visual image technology helps promote and develop Wushu. In order to segment and extract the signals of Wushu movements, this study analyzes the denoising of the original data using the wavelet transform and provides a sliding window data segmentation technique. Wushu movement The Wushu movement recognition model is built based on the hidden Markov model (HMM). The HMM model is trained and taught with the help of the Baum-Welch algorithm, which is then enhanced using the frequency weighted training approach and the mean training method. To identify the dynamic Wushu movement, the Viterbi algorithm is used to determine the probability of the optimal state sequence for each Wushu movement model. In light of the foregoing, an HMM-based martial arts movements recognition model is developed. The recognition accuracy of the HMM model increases to 99.60% when the number of samples is 4,000, which is greater than the accuracy of the SVM (by 0.94%), the CNN (by 1.12%), and the BP (by 1.14%). From what has been discussed, it appears that the suggested system for detecting martial arts acts is trustworthy and effective, and that it may contribute to the growth of martial arts.

• Earthquakes and Structures
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• 제25권1호
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• pp.43-56
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• 2023

Banda Aceh is one of the areas that sustains the most damage during a natural disaster because it contains so many houses, office buildings, public facilities, and schools. Public structures in coastal areas are highly susceptible to earthquakes, resulting in high casualties and property damage. Several public structures were reconstructed during the reconstruction and rehabilitation period. Because this building is located in an area with a high risk of earthquakes, its capacity must be analyzed initially. Additionally, history indicates that Aceh Province has been struck by numerous earthquakes, including the largest ever recorded in 1983 and the most recent earthquake with a magnitude of 9.3 SR on December 26, 2004. The city of Banda Aceh was devastated by this earthquake, which was followed by a tsunami. The possibility of a large earthquake in Banda Aceh City necessitates that the structures constructed there be resistant to seismic risk. This study's objective was to evaluate the seismic performance of the existing building by applying the method of strengthening the structure in the form of jacketing columns and the addition of steel bracing in order to estimate the performance of the structure using multiple ground motions. Therefore, several public buildings must be analyzed to determine the optimal seismic retrofitting technique.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2022년도 학술발표회
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• pp.149-149
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• 2022

Accurate quantitative evaluation of baseflow contribution to streamflow is imperative to address seasonal drought vulnerability, flood occurrence and groundwater management concerns for efficient and sustainable water resources management in watersheds. Several baseflow separation algorithms using recursive filters, graphical method and tracer or chemical balance have been developed but resulting baseflow outputs always show wide variations, thereby making it hard to determine best separation technique. Therefore, the current global shift towards implementation of artificial intelligence (AI) in water resources is employed to compare the performance of deep learning models with conventional hydrograph separation techniques to quantify baseflow contribution to streamflow of Piney River watershed, Tennessee from 2001-2021. Streamflow values are obtained from the USGS station 03602500 and modeled to generate values of Baseflow Index (BI) using Web-based Hydrograph Analysis (WHAT) model. Annual and seasonal baseflow outputs from the traditional separation techniques are compared with results of Long Short Term Memory (LSTM) and simple Gated Recurrent Unit (GRU) models. The GRU model gave optimal BFI values during the four seasons with average NSE = 0.98, KGE = 0.97, r = 0.89 and future baseflow volumes are predicted. AI offers easier and more accurate approach to groundwater management and surface runoff modeling to create effective water policy frameworks for disaster management.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제46권4호
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• pp.173-180
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• 2023

Smart factory companies are installing various sensors in production facilities and collecting field data. However, there are relatively few companies that actively utilize collected data, academic research using field data is actively underway. This study seeks to develop a model that detects anomalies in the process by analyzing spindle power data from a company that processes shafts used in automobile throttle valves. Since the data collected during machining processing is time series data, the model was developed through unsupervised learning by applying the Holt Winters technique and various deep learning algorithms such as RNN, LSTM, GRU, BiRNN, BiLSTM, and BiGRU. To evaluate each model, the difference between predicted and actual values was compared using MSE and RMSE. The BiLSTM model showed the optimal results based on RMSE. In order to diagnose abnormalities in the developed model, the critical point was set using statistical techniques in consultation with experts in the field and verified. By collecting and preprocessing real-world data and developing a model, this study serves as a case study of utilizing time-series data in small and medium-sized enterprises.