• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.159-167
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• 2015

We consider a wafer lot transfer/release planning problem from semiconductor wafer fabrication facilities to probing facilities with the objective of minimizing the deviation of workload and total tardiness of customers' orders. Due to the complexity of the considered problem, we propose a two-level hierarchical production planning method for the lot transfer problem between two parallel facilities to obtain an executable production plan and schedule. In the higher level, the solution for the reduced mathematical model with Lagrangian relaxation method can be regarded as a coarse good lot transfer/release plan with daily time bucket, and discrete-event simulation is performed to obtain detailed lot processing schedules at the machines with a priority-rule-based scheduling method and the lot transfer/release plan is evaluated in the lower level. To evaluate the performance of the suggested planning method, we provide computational tests on the problems obtained from a set of real data and additional test scenarios in which the several levels of variations are added in the customers' demands. Results of computational tests showed that the proposed lot transfer/planning architecture generates executable plans within acceptable computational time in the real factories and the total tardiness of orders can be reduced more effectively by using more sophisticated lot transfer methods, such as considering the due date and ready times of lots associated the same order with the mathematical formulation. The proposed method may be implemented for the problem of job assignment in back-end process such as the assignment of chips to be tested from assembly facilities to final test facilities. Also, the proposed method can be improved by considering the sequence dependent setup in the probing facilities.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.207-213
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• 2021

Recently, the number and scale of projects being carried out within the enterprise are increasing. Accordingly, many companies are competitively introducing a Project Management Office (PMO) to efficiently manage these projects, allocate resources, and effectively link the projects and corporate strategies. However, the project manager who directly manages the project wants to receive support from the PMO in many areas for successful project management, but the project manager does not like to be interfered with by the PMO. On the other hand, the PMO may not be able to satisfy all the requirements of each PM as PMO oversees the entire project with limited resources. In addition, since the PMO must monitor and control the project and support the project according to the priorities of each project, conflicts with each PM can be formed. Therefore, in this study, based on the case of a company, the difference between the perceptions of PMs and PMOs about the importance of the required roles of PMOs is to be examined. As a result of the study, it was confirmed that the core functions and sub-functions of PMO, which PMO members and PMs consider important, are different. It was identified that the PMs valued the PMO function that would be helpful for their successful project execution. On the other hand, PMO members revealed that they had a relatively high priority for the roles to monitor and control project performance for which they were directly responsible.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.865-889
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• 2019

The need for cyber resilience is increasingly important in our technology-dependent society where computing devices and data have been, and will continue to be, the target of cyber-attackers, particularly advanced persistent threat (APT) and nation-state/sponsored actors. APT and nation-state/sponsored actors tend to be more sophisticated, having access to significantly more resources and time to facilitate their attacks, which in most cases are not financially driven (unlike typical cyber-criminals). For example, such threat actors often utilize a broad range of attack vectors, cyber and/or physical, and constantly evolve their attack tactics. Thus, having up-to-date and detailed information of APT's tactics, techniques, and procedures (TTPs) facilitates the design of effective defense strategies as the focus of this paper. Specifically, we posit the importance of taxonomies in categorizing cyber-attacks. Note, however, that existing information about APT attack campaigns is fragmented across practitioner, government (including intelligence/classified), and academic publications, and existing taxonomies generally have a narrow scope (e.g., to a limited number of APT campaigns). Therefore, in this paper, we leverage the Cyber Kill Chain (CKC) model to "decompose" any complex attack and identify the relevant characteristics of such attacks. We then comprehensively analyze more than 40 APT campaigns disclosed before 2018 to build our taxonomy. Such taxonomy can facilitate incident response and cyber threat hunting by aiding in understanding of the potential attacks to organizations as well as which attacks may surface. In addition, the taxonomy can allow national security and intelligence agencies and businesses to share their analysis of ongoing, sensitive APT campaigns without the need to disclose detailed information about the campaigns. It can also notify future security policies and mitigation strategy formulation.

• Steel and Composite Structures
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• v.38 no.3
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• pp.337-353
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• 2021

Micro-electro-mechanical systems (MEMS) are widely employed in sensors, biomedical devices, optic sectors, and micro-accelerometers. New reinforcement materials such as carbon nanotubes as well as graphene platelets provide stiffer structures with controllable mechanical specifications by changing the graphene platelet features. This paper deals with buckling analyses of functionally graded graphene platelets micro plates with two piezoelectric layers subjected to external applied voltage. Governing equations are based on Kirchhoff plate theory assumptions beside the modified couple stress theory to incorporate the micro scale influences. A uniform temperature change and external electric field are regarded along the micro plate thickness. Moreover, an external in-plane mechanical load is uniformly distributed along the micro plate edges. The Hamilton's principle is employed to extract the governing equations. The material properties of each composite layer reinforced with graphene platelets of the considered micro plate are evaluated by the Halpin-Tsai micromechanical model. The governing equations are solved by the Navier's approach for the case of simply-supported boundary condition. The effects of the external applied voltage, the material length scale parameter, the thickness of the piezoelectric layers, the side, the length and the weight fraction of the graphene platelets as well as the graphene platelets distribution pattern on the critical buckling temperature change and on the critical buckling in-plane load are investigated. The outcomes illustrate the reduction of the thermal buckling strength independent of the graphene platelets distribution pattern while meanwhile the mechanical buckling strength is promoted. Furthermore, a negative voltage, -50 Volt, strengthens the micro plate stability against the thermal buckling occurrence about 9% while a positive voltage, 50 Volt, decreases the critical buckling load about 9% independent of the graphene platelet distribution pattern.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.30-36
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• 2022

This study analyzed the relationship between efficient pitchers and teams advancing to the postseason in Korean professional baseball through DEA. A total of 1,133 pitchers who threw more than one inning from the 2014 season to the 2018 season were selected for this study. For DEA analysis, input variables were selected as annual salary and inning output variables as Wins, Saves, and Holds and the number of efficient pitchers for each season was classified using the input-oriented BCC model. After that, it was divided into two groups based on joining the postseason or not, and the number of efficient pitchers was compared through a prop test. As a result of the analysis, the groups that advanced to the postseason in the rest of the season except for the 2014 and 2017 seasons had more efficient pitchers. Considering that the 2014 season recorded the highest WAR (Wins Above Replacement) at 183.56 compared to other seasons, most pitchers threw well, and in the 2017 season, they made more mistakes in pitching than in other seasons, but they performed well in batters. The results of this study have expanded the research field using efficiency analysis in professional baseball and can be used as useful data for practical research.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.177-189
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• 2023

Malware detection is an increasingly important operational focus in cyber security, particularly given the fast pace of such threats (e.g., new malware variants introduced every day). There has been great interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis. In this paper, we present a deep recurrent neural network solution as a stacked Long Short-Term Memory (LSTM) with a pre-training as a regularization method to avoid random network initialization. In our proposal, we use global and short dependencies of the inputs. With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting. The proposed method speeds up the convergence (in comparison to stacked LSTM) by reducing the length of malware OpCode or bytecode sequences. Hence, the complexity of our final method is reduced. This leads to better accuracy, higher Mattews Correlation Coefficients (MCC), and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time. Our proposed method can be applied in real-time malware threat hunting, particularly for safety critical systems such as eHealth or Internet of Military of Things where poor convergence of the model could lead to catastrophic consequences. We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis. For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method. More specifically, of our proposed method achieves an accuracy of 99.1% in detecting IoT malware samples, with AUC of 0.985, and MCC of 0.95; thus, outperforming standard LSTM based methods in these key metrics.

• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.55-60
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• 2023

Recently, demand for cloud computing has increased and remote access due to home work and external work has increased. In addition, a new security paradigm is required in the current situation where the need to be vigilant against not only external attacker access but also internal access such as internal employee access to work increases and various attack techniques are sophisticated. As a result, the network security model applying Zero-Trust, which has the core principle of doubting everything and not trusting it, began to attract attention in the security industry. Zero Trust Security monitors all networks, requires authentication in order to be granted access, and increases security by granting minimum access rights to access requesters. In this paper, we explain zero trust and zero trust architecture, and propose a new cloud security system for strengthening access control that overcomes the limitations of existing security systems using zero trust and blockchain and can be used by various companies.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.561-566
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• 2011

In general, the early stage of urban renewal such as preparing a master plan and processing administrative works including planning permission are conducted by local governments in Korea. The local governments need to review the status of projects that are undergone in other local governments' territories. However, no integrated information system to manage information to this end at the level of nation exists in Korea. If the system would be developed, it may support central government to obtain information on required resources at the national level. In addition, local governments can gain guidance on the process and recognize potential problematic situations from others experience. The system should include functions to collect data on project summary, cost and schedule of projects according to local governments. The expected effects from using the information system are as following. First, information generated from project practice become more credible on account of management at the national level. Because the authorized party such as system administrative agents of governments are responsible for collecting and managing data. Second, the unified information system with no regard to the place where projects progresses reduces the efforts for accumulating reference data for aiding local governments decision making by providing appropriate information timely. Also, enhanced information accessibility for stakeholders make the project process clear. Finally, oversight management is enforced with visualization technology adopted in the system, presenting master plan and mass model including information on usage by floors and progressing information graphically. Ultimately, potential challenges can be anticipated by considering records accumulated from other local governments' projects. This paper presents concept, functionalities, and architecture of information system enabling to manage data from individual projects and aggregate those for oversight management for local and central governments. As a part of systems analysis, general requirements of briefing system for governments and necessary data fields to this end are identified.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.163-177
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• 2024

The fingerprint image quality relies on the clearness of separated ridges by valleys and the uniformity of the separation. The condition of skin still dominate the overall quality of the fingerprint. However, the identification performance of such system is very sensitive to the quality of the captured fingerprint image. Fingerprint image quality analysis and enhancement are useful in improving the performance of fingerprint identification systems. A fuzzy technique is introduced in this paper for both fingerprint image quality analysis and enhancement. First, the quality analysis is performed by extracting four features from a fingerprint image which are the local clarity score (LCS), global clarity score (GCS), ridge_valley thickness ratio (RVTR), and the Global Contrast Factor (GCF). A fuzzy logic technique that uses Mamdani fuzzy rule model is designed. The fuzzy inference system is able to analyse and determinate the fingerprint image type (oily, dry or neutral) based on the extracted feature values and the fuzzy inference rules. The percentages of the test fuzzy inference system for each type is as follow: For dry fingerprint the percentage is 81.33, for oily the percentage is 54.75, and for neutral the percentage is 68.48. Secondly, a fuzzy morphology is applied to enhance the dry and oily fingerprint images. The fuzzy morphology method improves the quality of a fingerprint image, thus improving the performance of the fingerprint identification system significantly. All experimental work which was done for both quality analysis and image enhancement was done using the DB_ITS_2009 database which is a private database collected by the department of electrical engineering, institute of technology Sepuluh Nopember Surabaya, Indonesia. The performance evaluation was done using the Feature Similarity index (FSIM). Where the FSIM is an image quality assessment (IQA) metric, which uses computational models to measure the image quality consistently with subjective evaluations. The new proposed system outperformed the classical system by 900% for the dry fingerprint images and 14% for the oily fingerprint images.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.3
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• pp.121-128
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• 2024

There are growing concerns that the recently implemented Earthquake Early Warning service is overestimating the rapidly provided earthquake magnitudes (M). As a result, the predicted damages unnecessarily activate earthquake protection systems for critical facilities and lifeline infrastructures that are far away. This study is conducted to improve the estimation accuracy of M by incorporating the observed S-wave seismograms in the near source region after removing the site effects of the seismograms in real time by filtering in the time domain. The ensemble of horizontal S-wave spectra from at least five seismograms without site effects is calculated and normalized to a hypocentric target distance (21.54 km) by using the distance attenuation model of Q(f)=348f^0.52 and a cross-over distance of 50 km. The natural logarithmic mean of the S-wave ensemble spectra is then fitted to Brune's source spectrum to obtain the best estimates for M and stress drop (SD) with the fitting weight of 1/standard deviation. The proposed methodology was tested on the 18 recent inland earthquakes in South Korea, and the condition of at least five records for the near-source region is sufficiently fulfilled at an epicentral distance of 30 km. The natural logarithmic standard deviation of the observed S-wave spectra of the ensemble was calculated to be 0.53 using records near the source for 1~10 Hz, compared to 0.42 using whole records. The result shows that the root-mean-square error of M and ln(SD) is approximately 0.17 and 0.6, respectively. This accuracy can provide a confidence interval of 0.4~2.3 of Peak Ground Acceleration values in the distant range.