• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.127-133
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• 2020

As the usages of artificial intelligence is increased, the demand to algorithms for small portable devices increases. Also as the embedded system becomes high-performance, it is possible to implement algorithms for high-speed computation and machine learning as well as operating systems. As the machine learning algorithms process repetitive calculations, it depend on the cloud environment by network connection. For an stand alone system, low power consumption and fast execution by optimized algorithm are required. In this study, for the purpose of smart control, an energy measurement sensor is connected to an embedded system, and a real-time monitoring system is implemented to store measurement information as a database. Continuously measured and stored data is applied to a learning algorithm, which can be utilized for optimal power control, and a system interfacing various sensors required for energy measurement was constructed.

• Food Science of Animal Resources
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• v.43 no.3
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• pp.428-440
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• 2023

Global meat consumption is increasing worldwide, however, supply remains lacking. Several alternative protein sources, such as cultured meat, plant-based protein production, and edible insects, have been proposed to overcome this shortage. Interestingly, edible insects are characterized by superior digestive and absorptive qualities that make them the ideal replacement for traditional protein production. This study aims to further the processing ability of insect protein by investigating the effects of various pre-treatment methods, such as blanching (HB), roasting (HR), and superheated steam (HS), on the nutritional properties and physicochemical characteristics of proteins extracted from Hermetia illucens larvae. The drying rate, pH value, color analysis, amino and fatty acid profile, as well as bulk density, shear force, and rehydration ratios of the above pre-treatment methods, were explored. HS was found to have the highest drying rate and pH value analysis showed that HB and HS samples have significantly higher values compared to the other modalities. Raw edible insects had the highest value in the sum of essential amino acid (EAA) and EAA index when compared to EAAs. HB and HS showed significantly lower bulk density results, and HS showed the highest shear force and the highest value in rehydration ratio, regardless of immersion time. Therefore, taking the above results together, it was found that blanching and superheated steam blanching pre-treatment were the most effective methods to improve the processing properties of H. illucens after hot-air drying.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2188-2208
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• 2023

Quality of Service (QoS) is a critical feature of Wireless Sensor Networks (WSNs) with routing algorithms. Data packets are moved between cluster heads with QoS using a number of energy-efficient routing techniques. However, sustaining high scalability while increasing the life of a WSN's networks scenario remains a challenging task. Thus, this research aims to develop an energy-balancing component that ensures equal energy consumption for all network sensors while offering flexible routing without congestion, even at peak hours. This research work proposes a Gravitational Blackhole Search Optimised splay tree routing framework. Based on the splay tree topology, the routing procedure is carried out by the suggested method using three distinct steps. Initially, the proposed GBSO decides the optimal route at initiation phases by choosing the root node with optimum energy in the splay tree. In the selection stage, the steps for energy update and trust update are completed by evaluating a novel reliance function utilising the Parent Reliance (PR) and Grand Parent Reliance (GPR). Finally, in the routing phase, using the fitness measure and the minimal distance, the GBSO algorithm determines the best route for data broadcast. The model results demonstrated the efficacy of the suggested technique with 99.52% packet delivery ratio, a minimum delay of 0.19 s, and a network lifetime of 1750 rounds with 200 nodes. Also, the comparative analysis ensured that the suggested algorithm surpasses the effectiveness of the existing algorithm in all aspects and guaranteed end-to-end delivery of packets.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.7
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• pp.865-874
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• 2017

According to Mental accounting theory, humans sort money according to their psychological purpose. It plays a major role as a means of self-control. However, humans sometimes make mistakes that violate very simple economic principles. Many consumers experience this mistakes. So consumers use account book to manage their income and expenses. These days, they use mobile account book applications. And because of platform characteristics, there are various user interfaces. This paper examines an efficient user interface design that can reduce errors caused by mental accounting and support rational economic activities. For this, we compared and analyzed households exposed on top of Application store based on advanced research on Mental Accounting. Through this case study, We found some cases of efficient user interface for reasonable consumption. In further studies, We will design optimal account book's UI and do a usability test based on this.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.2
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• pp.81-88
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• 2023

SHM (Structural Health Monitoring) technique for monitoring aircraft structural health and damage, EHM (Engine Health Monitoring) for monitoring aircraft engine performance, and APM (Application Performance Management) is used for each function. APMS (Airplane Performance Monitoring System) is a program that comprehensively applies these techniques to identify the difference between the performance manual provided by the manufacturer and the actual fuel mileage of the aircraft and reflect it in the flight plan. The main purpose of using APMS is to understand the performance of each aircraft, to plan and execute flights in an optimal way, and consequently to reduce fuel consumption. First, it is to check the fuel efficiency trend of each aircraft, check the correlation between the maintenance work performed and the fuel mileage, find the cause of the fuel mileage increase/decrease, and take appropriate measures in response. Second, it is to find the cause of fuel mileage degradation in detail by checking the trends by engine performance and fuselage drag effect. Third, the APMS is to be used in making maintenance work decisions. Through APMS, aircraft with below average fuel mileage are identified, the cause of fuel mileage degradation is identified, and appropriate corrective actions are determined. Fourth, APMS data is used to analyze the economic analysis of equipment installation investment. The cost can be easily calculated as the equipment installation cost, but the benefit is fuel efficiency improvement, and the only way to check this is the manufacturer's theory. Therefore, verifying the effect after installation and verifying the economic analysis is to secure the appropriateness of the investment. Through this, proper investment in fuel efficiency improvement equipment will be made, and fuel efficiency will be improved.

• Journal of Industrial Convergence
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• v.21 no.8
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• pp.51-58
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• 2023

Blockchains have recently struggled to design for the ideal distributed trust networks by solving scalability trilemma. However, local conflicts between some countries lead to imbalance on energy distribution. Besides, blockchain networks (e.g., Bitcoin) currently consume enormous energy for transaction and mining. The existing data volume based trust model evaluated an increasing blockchain size better than Lubin's trust model in scalability trilemma. In this paper, we propose a scalability based energy model to evaluate sustainability for blockchain networks, considering energy consumption for transaction, time duration, and the blockchain size of growing blockchain networks. Through the rigorous numerical analysis, we compare the proposed scalability based energy model with the existing model for the satisfaction and optimal blockchain size. Thus, the scalability based energy model will provide an assessment tool to choose the proper blockchain networks to solve scalability trilemma problem and prove sustainability.

• Membrane Journal
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• v.33 no.5
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• pp.257-268
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• 2023

Membrane capacitive deionization (MCDI) is a variation of the conventional CDI process that can improve desalination efficiency by employing an ion-exchange membrane (IEM) together with a porous carbon electrode. The IEM is a key component that greatly affects the performance of MCDI. In this study, we attempted to derive the optimal fabricating factors for IEMs that can significantly improve the desalination efficiency of MCDI. For this purpose, pore-filled IEMs (PFIEMs) were then fabricated by filling the pores of the PE porous support film with monomers and carrying out in-situ photopolymerization. As a result of the experiment, the prepared PFIEMs showed excellent electrochemical properties that can be applied to various desalination and energy conversion processes. In addition, through the correlation analysis between MCDI performance and membrane characteristic parameters, it was found that controlling the degree of crosslinking of the membranes and maximizing permselectivity within a sufficiently low level of membrane electrical resistance are the most desirable membrane fabricating condition for improving MCDI performance.

• Earthquakes and Structures
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• v.26 no.1
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• pp.77-86
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• 2024

To investigate the seismic performance of steel pipe-aeolian sand recycled concrete columns, this study designed and produced five specimens. Low-cycle repeated load tests were conducted while maintaining a constant axial compression ratio. The experiment aimed to examine the impact of different aeolian sand replacement rates on the seismic performance of these columns. The test results revealed that the mechanical failure modes of the steel pipe-recycled concrete column and the steel pipe-aeolian sand recycled concrete column were similar. Plastic hinges formed and developed at the column foot, and severe local buckling occurred at the bottom of the steel pipe. Interestingly, the bulging height of the damaged steel pipe was reduced for the specimen mixed with an appropriate amount of wind-deposited sand under the same lateral displacement. The hysteresis curves of all five specimens tested were relatively full, with no significant pinching phenomenon observed. Moreover, compared to steel tube-recycled concrete columns, the steel tube-aeolian sand recycled concrete columns exhibited improved seismic energy dissipation capacity and ductility. However, it was noted that as the aeolian sand replacement rate increased, the bearing capacity of the specimen increased first and then decreased. The seismic performance of the specimen was relatively optimal when the aeolian sand replacement rate was 30%. Upon analysis and comparison, the damage analysis model based on stiffness and energy consumption showed good agreement with the test results and proved suitable for evaluating the damage degree of steel pipe-wind-sand recycled concrete structures.

• Korean Circulation Journal
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• v.53 no.6
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• pp.406-417
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• 2023

Background and Objectives: Pathophysiological changes of right ventricle (RV) after repair of tetralogy of Fallot (TOF) are coupled with a highly compliant low-pressure pulmonary artery (PA) system. This study aimed to determine whether pulmonary vascular function was associated with RV parameters and exercise capacity, and its impact on RV remodeling after pulmonary valve replacement. Methods: In a total of 48 patients over 18 years of age with repaired TOF, pulmonary arterial elastance (Ea), RV volume data, and RV-PA coupling ratio were calculated and analyzed in relation to exercise capacity. Results: Patients with a low Ea showed a more severe pulmonary regurgitation volume index, greater RV end-diastolic volume index, and greater effective RV stroke volume (p=0.039, p=0.013, and p=0.011, respectively). Patients with a high Ea had lower exercise capacity than those with a low Ea (peak oxygen consumption [peak VO₂] rate: 25.8±7.7 vs. 34.3±5.5 mL/kg/min, respectively, p=0.003), while peak VO₂ was inversely correlated with Ea and mean PA pressure (p=0.004 and p=0.004, respectively). In the univariate analysis, a higher preoperative RV end-diastolic volume index and RV end-systolic volume index, left ventricular end-systolic volume index, and higher RV-PA coupling ratio were risk factors for suboptimal outcomes. Preoperative RV volume and RV-PA coupling ratio reflecting the adaptive PA system response are important factors in optimal postoperative results. Conclusions: We found that PA vascular dysfunction, presenting as elevated Ea in TOF, may contribute to exercise intolerance. However, Ea was inversely correlated with pulmonary regurgitation (PR) severity, which may prevent PR, RV dilatation, and left ventricular dilatation in the absence of significant pulmonary stenosis.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.93-104
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• 2024

Demand response is a strategy that encourages customers to adjust their consumption patterns at times of peak demand with the aim to improve the reliability of the power grid and minimize expenses. The integration of renewable energy sources into smart grids poses significant challenges due to their intermittent and unpredictable nature. Demand response strategies, coupled with reinforcement learning techniques, have emerged as promising approaches to address these challenges and optimize grid operations where traditional methods fail to meet such kind of complex requirements. This research focuses on investigating the application of reinforcement learning algorithms in demand response for renewable energy integration. The objectives include optimizing demand-side flexibility, improving renewable energy utilization, and enhancing grid stability. The results emphasize the effectiveness of demand response strategies based on reinforcement learning in enhancing grid flexibility and facilitating the integration of renewable energy.