• Journal of Surface Science and Engineering
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• v.57 no.4
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• pp.338-347
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• 2024

𝛼-MnO₂ as a cathode material for Zn-ion batteries allows insertion and extraction of Zn ions within its tunnel structure during charge and discharge. The morphology and crystal structure of 𝛼-MnO₂ particles critically determine their electrochemical behavior and energy storage performance. In this study, 𝛼-MnO₂ was synthesized from precursor solutions under varying pH conditions using a hydrothermal method. The effects of pH values on the morphology, crystal structure, and electrochemical performance were systematically analyzed. The analysis revealed that materials synthesized at higher pH levels exhibited elongated and narrow nanorods with a lower specific surface area. In contrast, those formed at lower pH levels showed shorter, thicker nanorods with a higher specific surface area. This increased surface area at a lower pH enhanced the specific capacitance by providing a greater electrode/electrolyte interfacial area. By contrast, the material synthesized at higher pH conditions demonstrated superior rate capability, attributed to its crystal structure with wider lattice spacings. Wide lattice parameters in the material synthesized at higher pH conditions facilitated easier ion transport than at lower pH levels. Consequently, the study confirms that adjusting the pH of the precursor solution can optimize the electrochemical properties of 𝛼-MnO₂ for Zn-ion batteries.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.4
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• pp.49-72
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• 2020

In a rapidly changing environment, public libraries need performance measures that can comprehensively represent their activities in order to respond to various changes, demonstrate social value, and justify input resources. However, most of the performance evaluations have been focused on specific areas or specific analysis methods. Therefore, this study looked at the efforts of advanced countries of overseas libraries to develop performance evaluation tools for public libraries in order to develop performance management tools for domestic public libraries. The implications were derived by reviewing and analyzing the direction, evaluation system, and major performance evaluation areas of public library performance evaluation. The main result was that the perspective of public library performance evaluation was shifting from input-output-oriented to outcome or impact, and the performance evaluation area was linked to the development strategy of public libraries or the role of the library. And, the government and institutions were working together to establish an evaluation system for performance management of public libraries.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.229-247
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• 2023

With the increasing emphasis on anomaly detection across various fields, diverse anomaly detection algorithms have been developed for various data types and anomaly patterns. However, the performance of anomaly detection algorithms is generally evaluated on publicly available datasets, and the specific performance of each algorithm on anomalies of particular types remains unexplored. Consequently, selecting an appropriate anomaly detection algorithm for specific analytical contexts poses challenges. Therefore, in this paper, we aim to investigate the types of anomalies and various attributes of data. Subsequently, we intend to propose approaches that can assist in the selection of appropriate anomaly detection algorithms based on this understanding. Specifically, this study compares the performance of anomaly detection algorithms for four types of anomalies: local, global, contextual, and clustered anomalies. Through further analysis, the impact of label availability, data quantity, and dimensionality on algorithm performance is examined. Experimental results demonstrate that the most effective algorithm varies depending on the type of anomaly, and certain algorithms exhibit stable performance even in the absence of anomaly-specific information. Furthermore, in some types of anomalies, the performance of unsupervised anomaly detection algorithms was observed to be lower than that of supervised and semi-supervised learning algorithms. Lastly, we found that the performance of most algorithms is more strongly influenced by the type of anomalies when the data quantity is relatively scarce or abundant. Additionally, in cases of higher dimensionality, it was noted that excellent performance was exhibited in detecting local and global anomalies, while lower performance was observed for clustered anomaly types.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.659-662
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• 2009

In this paper described are the performance design guidelines of writing the items of foundation structures for designers. To accept the new performance code instead of existing design code in the construction market, performance design focuses on requirements to guidelines of foundation structure.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.10
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• pp.65-81
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• 1997

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.794-804
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• 2018

Various applications running in computers exchange information in the form of packets through the network. Most packets are formatted into UDP/IP or TCP/IP standard. Network management administrators of enterprises and organizations should be able to monitor and manage packets transmitted over the network for Internet traffic measurement & monitoring, network security, and so on. The goal of this paper is to analyze the performance of several algorithms which closely examine and analyze payloads in a DPI(Deep Packet Inspection) system. The main procedure of packet payload analysis is to quickly search for a specific pattern in a payload. In this paper, we introduce several algorithms which detect a specific pattern in payloads, analyze the performance of them from three perspectives, and suggest an application method suitable for requirements of a given DPI system.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.6
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• pp.948-953
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• 2007

The relative performance and immune response was evaluated in White Leghorn layers fed liquid DL-methionine hydroxyl analogue-free acid (MHA-FA) relative to dry DL-methionine (DLM) in maize-soybean-sunflower based diets. Three graded levels of methionine (Met) from DLM or MHA-FA were added to the basal diet containing 0.27% Met on an equimolar basis to achieve 0.30, 0.36 and 0.42% Met in the diet. Each diet was fed ad libitum to 25 replicates of one bird (individual feeding) each, from 24 to 40 weeks of age. A regime of 16 h light was provided and all the layers were kept under uniform management throughout the experimental period. None of the parameters studied were influenced by the interaction between source and level of Met in diets. Similarly, the majority of parameters, except for daily feed consumption and immune response (influenced by level) and egg specific gravity and shell thickness (influenced by source), were not affected by either source or level of Met in the diets. Feed consumption was significantly lower in the birds fed a diet containing 0.42% Met compared to those fed lower levels of Met. The cutaneous basophilic hypersensitivity response to PHA-P and antibody titre (32 and 40 wk) to inoculation of sheep red blood cells increased significantly by increasing the concentration of Met in the diet from 0.30 to 0.36%. Thus, the Met requirement for immune competence was higher than for optimum production. The source of Met significantly influenced the egg specific gravity and shell thickness. The specific gravity and shell thickness of eggs increased significantly when MHA-FA was used as the source of Met in the diet compared to DLM. From the study it is concluded that Met requirement for immune competence (360 mg/b/d) is higher than for optimum production (300 mg/b/d). MHA-FA was comparable with DLM as a source of Met for production performance and immunity, when the bioavailability of MHA-FA was considered as 88% of DLM. Further, MHA-FA improved egg shell quality compared to DLM.

• Journal of the Korean Society of Propulsion Engineers
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• v.3 no.1
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• pp.1-8
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• 1999

In the present paper an attempt has been made to simulate the secondary injection-primary flow interaction in the conical rocket nozzle and to derive the performance of secondary injection thrust vector control(SITVC) system. Complex three-dimensional flowfield induced by the secondary injection is numerically analyzed by solving unsteady three-dimensional Euler equation with Beam and Warming's implicit approximate factorization method. Emphasized in the present study is the effect of secondary injection such as secondary mass flow rates and the momentum of secondary/primary nozzle flow mass rates upon the gross system performance parameters such as thrust ratio, specific impulse ratio and deflection angle. The results obtained in terms of system performance parameters show that lower secondary mass flow rate is advantageous for to reduce secondary specific impulse loss. It is further found that the nozzle with secondary jet injected downstream and interacting with fast primary flow is preferable for efficient and stable SITVC over the wide range of use with the penalty of side specific impulse loss.

• 연구논문집
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• s.27
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• pp.87-99
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• 1997

Present paper describes on/off design performance of a 50KW turbogenerator gas turbine engine for hybrid vehicle application. For optimum design point selection, relevant parameter study is carried out. The turbogenerator gas turbine engine for a hybrid vehicle is expected to be designed for maximum fuel economy, ultra low emissions, and very low cost. Compressor, combustor, turbine, and permanent-magnet generator will be mounted on a single high speed (82,000 rpm) shaft that will be supported on air bearings. As the generator is built into the shaft, gearbox and other moving parts become unnecessary and thus will increase the system's reliability and reduce the manufacturing cost. The engine has a radial compressor and turbine with design point pressure ratio of 4.0. This pressure ratio was set based on calculation of specific fuel consumption and specific power variation with pressure ratio. For the given turbine inlet temperature, a rather conservative value of $1100^\circK$ was selected. Designed mass flow rate was 0.5 kg/sec. Parametric study of the cycle indicates that specific work and efficiency increase at a given pressure ratio and turbine inlet temperature. Off design analysis shows that the gas turbine system reaches self operating condition at N/$N_{DP}$ = 0.53. Bleeding air for turbine stator cooling is omitted considering low TIT and for a simple geometric structure. Various engine performance simulations including, ambient temperature influence, surging at part load condition. Transient analysis were performed to secure the optimum engine operating characteristics. Surge margin throughout the performance analysis were maintained to be over 80% approximately. Validation of present results are yet to be seen as the performance tests are scheduled by the end of 1998 for comparison.