• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.39 no.9
• /
• pp.28-36
• /
• 2002

As cross-coupling capacitance generated in transmission line has been an important issue in VLSI world, a couple of ATPG algorithms has been proposed. However they were studied only for a simple single-line effect problem, so it cost so much time for an unsatisfying test generation efficiency. In this paper, we studied a noise model for multiple affected lines and generated test patterns in a short time. This paper proposes a crosstalk model affected by multiple tranmission lines and implemented an ATPG algorithm for detection of crosstalk noise faults. We modeled the crosstalk noise by multiple transmission line and made a truth table for this. We implemented an ATPG algorithm based on PODEM and revealed the results.

• Korean Journal of Agricultural Science
• /
• v.47 no.3
• /
• pp.633-644
• /
• 2020

Rice paper is an element of Vietnamese cuisine that can be used to wrap vegetables and meat. Rice and starch are the main ingredients of rice paper and their mixing ratio is important for quality control. In a commercial factory, assessment of food safety and quantitative supply is a challenging issue. A rapid and non-destructive monitoring system is therefore necessary in commercial production systems to ensure the food safety of rice and starch flour for the rice paper wrap. In this study, fluorescence hyperspectral imaging technology was applied to classify grain flours. Using the 3D hyper cube of fluorescence hyperspectral imaging (fHSI, 420 - 730 nm), spectral and spatial data and chemometric methods were applied to detect and classify flours. Eight flours (rice: 4, starch: 4) were prepared and hyperspectral images were acquired in a 5 (L) × 5 (W) × 1.5 (H) cm container. Linear discriminant analysis (LDA), partial least square discriminant analysis (PLSDA), support vector machine (SVM), classification and regression tree (CART), and random forest (RF) with a few preprocessing methods (multivariate scatter correction [MSC], 1^st and 2^nd derivative and moving average) were applied to classify grain flours and the accuracy was compared using a confusion matrix (accuracy and kappa coefficient). LDA with moving average showed the highest accuracy at A = 0.9362 (K = 0.9270). 1D convolutional neural network (CNN) demonstrated a classification result of A = 0.94 and showed improved classification results between mimyeon flour (MF)1 and MF2 of 0.72 and 0.87, respectively. In this study, the potential of non-destructive detection and classification of grain flours using fHSI technology and machine learning methods was demonstrated.

• Smart Structures and Systems
• /
• v.21 no.3
• /
• pp.287-303
• /
• 2018

Applications of energy harvesting from mechanical vibrations is becoming popular but the full potential of such applications is yet to be explored. This paper addresses this issue by considering an application of energy harvesting for the dual objective of serving as an indicator of structural health monitoring (SHM) and extent of control. Variation of harvested energy from an undamaged baseline is employed for this purpose and the concept is illustrated by implementing it for active vibrations of a pipe structure. Theoretical and experimental analyses are carried out to determine the energy harvesting potential from undamaged and damaged conditions. The use of energy harvesting as indicator for control is subsequently investigated, considering the effect of the introduction of a tuned mass damper (TMD). It is found that energy harvesting can be used for the detection and monitoring of the location and magnitude of damage occurring within a pipe structure. Additionally, the harvested energy acts as an indicator of the extent of reduction of vibration of pipes when a TMD is attached. This paper extends the range of applications of energy harvesting devices for the monitoring of built infrastructure and illustrates the vast potential of energy harvesters as smart sensors.

• The KIPS Transactions:PartD
• /
• v.8D no.6
• /
• pp.835-842
• /
• 2001

We propose a software-reliability growth model incoporating the amount of testing effort expended during the software testing phase. The time-dependent behavior of testing effort expenditures is described by a Weibull curve. Assuming that the error detection rate to the amount of testing effort spent during the testing phase is proportional to the current error content, a software-reliability growth model is formulated by a nonhomogeneous Poisson process. Using this model the method of data analysis for software reliability measurement is developed. After defining a software reliability, we discuss the relations between testing time and reliability and between duration following failure fixing and reliability are studied in this paper. The release time making the testing cost to be minimum is determined through studying the cost for each condition. Also, the release time is determined depending on the conditions of the specified reliability. The optimum release time is determined by simultaneously studying optimum release time issue that determines both the cost related time and the specified reliability related time.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.3
• /
• pp.418-424
• /
• 2018

Detection and classification of underwater targets is an important issue for both military and non-military purposes. Recently, many performance improvements are being reported in the field of pattern recognition with the development of deep learning technology. Among the results, DBN showed good performance when used for pre-training of DNN. In this paper, DBN was used for the classification of underwater targets using active sonar, and the results are compared with that of the conventional BPNN. We synthesized active sonar target signals using 3-dimensional highlight model. Then, features were extracted based on FrFT. In the single aspect based experiment, the classification result using DBN was improved about 3.83% compared with the BPNN. In the case of multi-aspect based experiment, a performance of 95% or more is obtained when the number of observation sequence exceeds three.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.11
• /
• pp.541-548
• /
• 2016

The use of social media is already essential as a source of information for listening user's various opinions and monitoring. We define social 'risks' that issues effect negative influences for public opinion in social media. This paper aims to discriminate various linguistic features and reveal their effects for building an automatic classification model of social risks. Expecially we adopt a word embedding technique for representation of linguistic clues in risk sentences. As a preliminary experiment to analyze characteristics of individual features, we revise errors in automatic linguistic analysis. At the result, the most important feature is NE (Named Entity) information and the best condition is when combine basic linguistic features. word embedding, and word clusters within core predicates. Experimental results under the real situation in social bigdata - including linguistic analysis errors - show 92.08% and 85.84% in precision respectively for frequent risk categories set and full test set.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.817-819
• /
• 2003

Land degradation is a crucial issue in mountainous areas and is manifested in a variety of processes. For its assessment, application of existing models is not straightforward. In addition, data availability might be a problem. In this paper, a procedure for land degradation assessment is described, which follows a four-step approach: (1) detection, inventory and mapping of land degradation features, (2) assessing the magnitude of soil loss, (3) study of causal factors, and (4) hazard assessment by applying decision trees. This approach is applied to a case study in the Middle Mountain region of Nepal. The study shows that individual mass movement features such as debris slides and slumps can be easily mapped by photo interpretation techniques. Application of soil loss estimation models helps get insight on the magnitude of soil losses. In the study area soil losses are higher in rainfed crops on sloping terraces (highest soil loss is 32 tons/ha/yr) and minimal under dense forest and in irrigated rice fields (less than 1 ton/ha/yr). However there is high frequency of slope failures in the form of slumps in the rice fields. Debris slides are more common on south-facing slopes under rainfed agriculture or in degraded forest. Field evidences and analysis of causal factors for land degradation helps in building decision trees, the use of which for modelling land degradation has the advantage that attributes can be ranked and tested according to their importance. In addition, decision trees are simple to construct, easy to implement and very flexible in adaptations.

• Korean Journal of Remote Sensing
• /
• v.25 no.6
• /
• pp.547-557
• /
• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• Clinical and Experimental Reproductive Medicine
• /
• v.48 no.4
• /
• pp.322-336
• /
• 2021

Objective: Endometriosis is a chronic debilitating inflammatory condition characterized by the presence of endometrial tissues outside the uterine cavity. Pelvic soreness and infertility are the usual association. Due to the poor effectiveness of the hormone therapy and the high incidence of recurrence following surgical excision, there is no single effective option for management of endometriosis. Mesenchymal stem cells (MSCs) are multipotent stromal cells studied for their broad immunoregulatory and anti-inflammatory properties; however, their efficiency in endometriosis cases is still a controversial issue. Our study aim was to evaluate whether adipose tissue-derived MSCs (AD-MSCs) could help with endometriosis through their studied anti-inflammatory role. Methods: Female Wistar rats weighting 180 to 250 g were randomly divided into two groups: group 1, endometriosis group; established by transplanting autologous uterine tissue into rats' peritoneal cavities and group 2, stem cell treated group; treated with AD-MSCs on the 5th day after induction of endometriosis. The proliferative activity of the endometriosis lesions was evaluated through Ki67 staining. Quantitative estimation of interferon γ, tumor necrosis factor-α, interleukin (IL)-6, IL-1β, IL-10, and transforming growth factor β expression, as well as immunohistochemical detection of CD68 positive macrophages, were used to assess the inflammatory status. Results: The size and proliferative activity of endometriosis lesions were significantly reduced in the stem cell treated group. Stem cells efficiently mitigated endometriosis associated chronic inflammatory reactions estimated through reduction of CD68 positive macrophages and the expression of the proinflammatory cytokines. Conclusion: Stem cell therapy can be considered a novel remedy in endometriosis possibly through its anti-inflammatory and antiproliferative properties.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.4
• /
• pp.739-751
• /
• 2019

Security in embedded devices has become a significant issue. Threats on the sup-ply chain, like using counterfeit components or inserting backdoors intentionally are one of the most significant issues in embedded devices security. To mitigate these threats, high-level security evaluation and certification more than EAL (Evaluation Assurance Level) 5 on CC (Common Criteria) are necessary on hardware components, especially on the cryptographic module such as AES. High-level security evaluation and certification require detecting covert channel such as backdoors on the cryptographic module. However, previous studies have a limitation that they cannot detect some kinds of backdoors which leak the in-formation recovering a secret key on the cryptographic module. In this paper, we present an expanded definition of backdoor on hardware AES module and show how to detect the backdoor which is never detected in Verilog HDL using model checker NuSMV.