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Renewable energy generation capacity in Korea is expected to reach about 63.8 GW by 2030 based on calculations using values from a power plan survey (Korea's renewable energy power generation project plan implemented in September 2017) and the &ldquo, 3020&rdquo, implementation plan prescribed in the 8th Basic Plan for Long-Term Electricity Supply and Demand that was announced in 2017. In order for the electrical grid to accommodate this capacity, an appropriate power system reinforcement plan is critical. In this paper, a variety of scenarios are constructed involving renewable energy capacity, interconnection measures and reinforcement measures. Based on these scenarios, the impacts of large-scale renewable energy connections on the future power systems are analyzed and a reinforcement plan is proposed based on the system assessment results. First, the scenarios are categorized according to their renewable energy interconnection capacity and electricity supply and demand, from which a database is established. A dynamic model based on inverter-based resources is applied to the scenarios here. The transmission lines, high-voltage direct current and flexible alternating current transmission systems are reinforced to increase the stability and capabilities of the power systems considered here. Reinforcement measures are derived for each stage of renewable penetration based on static and dynamic analysis processes. As a result, when large-scale renewable energy has penetrated some areas in the future in Korean power systems, the most stable systems could be optimally configured by applying interconnection measure two and reinforcement measure two as described here. To verify the performance of the proposed methodology, in this paper, comprehensive tests are performed based on predicted large-scale power systems in 2026 and 2031. Database creation and simulation are performed semi-automatically here using Power System Simulator for Engineering (PSS/E) and Python.

A distributed system guarantees the acceptance of Byzantine fault tolerance (BFT) for information transmission. Practical BFT (pBFT) and asynchronous BFT (aBFT) are among the various available forms of BFT. Distributed systems generally share information with all participating nodes. After information is shared, the systems reshare it. Thus, ensuring BFT consumes a considerable amount of time. Herein, we propose Decision search protocols that apply the gossip protocol, denoted by DecisionBFT, for distributed networks with guaranteed BFT. Each protocol in DecisionBFT is completely asynchronous and leaderless, it has an eventual consensus but no round-robin or proof-of-work. The core concept of the proposed technology is the consensus structure, which is based on the directed acyclic graph (DAG) and gossip protocol. In the most general form, each node in the algorithm has a set of k neighbors of most preference. When receiving transactions, a node creates and connects an event block with all its neighbors. Each event block is signed by the hashes of the creating node and its k peers. The consensus structure of the event blocks utilizes a DAG, which guarantees aBFT. The proposed framework uses Lamport timestamps and concurrent common knowledge. Further, an example of a Decision search algorithm, based on the gossip protocol DecisionBFT, is presented. The proposed DecisionBFT protocol can reach a consensus when 2/3 of all participants agree to an event block without any additional communication overhead. The DecisionBFT protocol relies on a cost function to identify the k peers and generate the DAG-based consensus structure. By creating a dynamic flag table that stores connection information between blocks, the gossip protocol achieves a consensus in fewer steps than that in the case of the existing aBFT protocol.

Social Internet of things is one of the most up-to-date research issues in the applications of Internet of things technologies. In social Internet of things, accuracy and reliability are standard features to discerning decisions. We assume that decision support systems based on social Internet of things could leverage research from recommender systems to achieve more stable performance. Therefore, we propose a trust-aware recommender systems suitable for social Internet of things. Trust-aware recommender systems adapt the concept of social networking service and utilize social interaction information. Trust information not only improves recommender systems from opinion spam problems but also more accurately predicts users’ preferences. We confirm that the performance of a recommender system becomes more improved when implicit trust is able to satisfy the properties of trust in the social Internet of things environment. The structure and amount of social link information are context-sensitive, so applying the concept of trust into social Internet of things environments requires a method to optimize implicit and explicit trust with minimal social link information. Our proposed method configures an asymmetric implicit trust network utilizing user–item rating matrix and transforms trust propagation metrics for a directional and weighted trust network. Through experiments, we confirm that the proposed methods enable higher accuracy and wider coverage compared to the existing recommendation methods.

Greenhouse gas (GHG) emission factors previously reported from various waste incineration plants have shown significant variations according to country-specific, plant-specific, and operational conditions. The purpose of this study is to estimate GHG emissions and emission factors at nine incineration facilities in Korea by measuring the GHG concentrations in the flue gas samples. The selected incineration plants had different operation systems (i.e., stoker, fluidized bed, moving grate, rotary kiln, and kiln & stoker), and different nitrogen oxide (NOx) removal systems (i.e., selective catalytic reduction (SCR) and selective non-catalytic reduction (SNCR)) to treat municipal solid waste (MSW), commercial solid waste (CSW), and specified waste (SW). The total mean emission factors for A and B facilities for MSW incineration were found to be 134 ± 17 kg CO2 ton-1, 88 ± 36 g CH4 ton-1, and 69 ± 16 g N2O ton-1, while those for CSW incineration were 22.56 g CH4 ton-1 and 259.76 g N2O ton-1, and for SW incineration emission factors were 2959 kg CO2 ton-1, 43.44 g CH4 ton-1 and 401.21 g N2O ton-1, respectively. Total emissions calculated using annual incineration for MSW were 3587 ton CO2-eq yr-1 for A facility and 11,082 ton CO2-eq yr-1 for B facility, while those of IPCC default values were 13,167 ton CO2-eq yr-1 for A facility and 32,916 ton CO2-eq yr-1, indicating that the emissions of IPCC default values were estimated higher than those of the plant-specific emission factors. The emission of CSW for C facility was 1403 ton CO2-eq yr-1, while those of SW for D to I facilities was 28,830 ton CO2-eq yr-1. The sensitivity analysis using a Monte Carlo simulation for GHG emission factors in MSW showed that the GHG concentrations have a greater impact than the incineration amount and flow rate of flue gas. For MSW incineration plants using the same stoker type in operation, the estimated emissions and emission factors of CH4 showed the opposite trend with those of NO2 when the NOx removal system was used, whereas there was no difference in CO2 emissions.

This work proposes a new adaptive sliding mode controller to enhance ride comfort and steering stability of automobile associated with a semi-active magneto-rheological damper. In this study, a Macpherson strut type suspension system which is widely used in light vehicles is considered. The dynamic model of the Macpherson strut with magneto-rheological damper is obtained and the governing equations are then formulated using kinematic properties of the suspension system following Lagrange’s formulation. In the formulation of the model, both the rotation of the wheel assembly and the lateral stiffness of the tire are considered to represent the nonlinear characteristic of Macpherson type suspension system. Subsequently, in order to effectively reduce unwanted vibrations, a new adaptive sliding mode controller is designed by adopting moving sliding surface instead of conventional fixed sliding surface. In order to demonstrate the effectiveness of the proposed controller, a cylindrical magneto-rheological damper is designed and manufactured on the basis of practical application conditions such as required damping force. Then, ride comfort, suspension travel, and road handling are evaluated and some benefits of the proposed controller such as enhanced ride comfort are evaluated.

One of the most popular applications for the recommender systems is a movie recommendation system that suggests a few movies to a user based on the user&rsquo, s preferences. Although there is a wealth of available data on movies, such as their genres, directors and actors, there is little information on a new user, making it hard for the recommender system to suggest what might interest the user. Accordingly, several recommendation services explicitly ask users to evaluate a certain number of movies, which are then used to create a user profile in the system. In general, one can create a better user profile if the user evaluates many movies at the beginning. However, most users do not want to evaluate many movies when they join the service. This motivates us to examine the minimum number of inputs needed to create a reliable user preference. We call this the magic number for determining user preferences. A recommender system based on this magic number can reduce user inconvenience while also making reliable suggestions. Based on user, item and content-based filtering, we calculate the magic number by comparing the accuracy resulting from the use of different numbers for predicting user preferences.

This paper presents a new hybrid controller which is a combination of three control schemes: fuzzy neural control, PI control and sliding mode control. The interval type 2 fuzzy model featuring updated rules via online is used in this study and in order to support the fuzzy model, a granular clustering method is applied to find groups of data related to the initial fuzzy rule. Then the output for fuzzy model is used for the PI-sliding mode controller. The combination of PI and sliding mode controls is carried out by H-infinity technique method which is rely on the modified Riccati-like equation. After developing the mathematical model, the proposed controller is applied to vibration control of a vehicle seat suspension featuring magneto-rheological (MR) damper. In order to demonstrate the effectiveness of the proposed controller, two different excitations of bump and random signals are adopted and corresponding vibration control performances are evaluated. It is demonstrated through both simulation and experiment that the proposed controller can provide much better than vibration control performance compared with the conventional controllers showing more robust stability.

This article presents the control performances of a vehicle seat suspension system equipped with magneto-rheological dampers using a new adaptive fuzzy sliding mode controller. A magneto-rheological damper is designed by applying the Bingham model incorporating with the field-dependent rheological properties of magneto-rheological fluid. On the other hand, a seat suspension model is established by integrating with a 4-degree-of-freedom human body model. Then, the governing equations are then derived considering the vertical motion of the seat. Subsequently, an adaptive fuzzy controller is formulated by considering the acceleration of the seat. This controller is combined with the sliding mode controller to ensure the robustness against model uncertainty and external disturbances. The controller is then evaluated through experiment. It is demonstrated that the proposed seat suspension system realized by the proposed adaptive fuzzy sliding mode controller can provide very effective ride comfort performances by reducing vertical acceleration under regular bump, random signal, and sinusoidal excitations.

An experimental study has been performed on natural convection heat transfer with and without crust formation in a molten metal pool. Two types of steady-state tests, natural and forced convection coolings of the molten metal pool, were performed in low- and high-aspect-ratio cases. When the natural convection flow is developed in the molten metal pool, the overlying coolant conditions do not affect the crust formation. On the other hand, when the natural convection flow is not developed, the coolant conditions affect the crust formation. The heat transfer rate of cases with crust formation is lower than that of cases without crust formation due to the effect of the crust serving as a thermal barrier. The present experimental results on the relationship between the Nusselt number and Rayleigh number match better with Globe and Dropkin's correlation than any others. With an increase in the crust thickness, the Nusselt number in the metal pool does not rapidly decrease in spite of a rapid decrease in Rayleigh number because the aspect ratio of the metal pool decreases. A new correlation between the Nusselt number and Rayleigh number in the molten metal pool with crust formation has been developed as Nu = 0.0923 (Ra) 0.302 (2 × 10 4 < Ra < 2 × 10 7 ).