• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.111-120
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• 2017

This paper proposes an improved standard genetic algorithm (GA) of making a near optimal schedule for integrated process planning and scheduling problem (IPPS) considering tool flexibility and tool related constraints. Process planning involves the selection of operations and the allocation of resources. Scheduling, meanwhile, determines the sequence order in which operations are executed on each machine. Due to the high degree of complexity, traditionally, a sequential approach has been preferred, which determines process planning firstly and then performs scheduling independently based on the results. The two sub-problems, however, are complicatedly interrelated to each other, so the IPPS tend to solve the two problems simultaneously. Although many studies for IPPS have been conducted in the past, tool flexibility and capacity constraints are rarely considered. Various meta-heuristics, especially GA, have been applied for IPPS, but the performance is yet satisfactory. To improve solution quality against computation time in GA, we adopted three methods. First, we used a random circular queue during generation of an initial population. It can provide sufficient diversity of individuals at the beginning of GA. Second, we adopted an inferior selection to choose the parents for the crossover and mutation operations. It helps to maintain exploitation capability throughout the evolution process. Third, we employed a modification of the hybrid scheduling algorithm to decode the chromosome of the individual into a schedule, which can generate an active and non-delay schedule. The experimental results show that our proposed algorithm is superior to the current best evolutionary algorithms at most benchmark problems.

• Journal of the Korea Society of Computer and Information
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• v.15 no.8
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• pp.23-30
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• 2010

Flash memory is non-volatile and can retain data even after system is powered off. Besides, it has many other features such as fast access speed, low power consumption, attractive shock resistance, small size, and light-weight. As its price decreases and capacity increases, the flash memory is expected to be widely used in consumer electronics, embedded systems, and mobile devices. Flash storage systems generally adopt a software layer, called FTL. In this research, we proposed a new FTL mechanism for overcoming the major drawback of conventional block mapping algorithm. In addition to the block mapping table, a index block mapping table with a small size is used to indicate sector location. The proposed indexed block mapping algorithm by adding a small size. By the simulation result, the proposed FTL provides an enhanced speed than a conventional hybrid mapping algorithm by around 45% in average, and the requirement of mapping memory is also reduced by around 12%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.3
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• pp.453-460
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• 2014

Fault injection attack is the process of attempting to acquire the information on-chip through inject artificially generated error code into the cryptographic algorithms operation (or perform) which is implemented in hardware or software. From the details above, the laser-assisted failure injection attacks have been proven particularly successful. In this paper, we propose an improved laser probing system for fault injection attack which is called the Dual-Laser FA tool set, a hybrid approach of the Flash-pumping laser and fiber laser. The main concept of the idea is to improve the laser probe through utilizing existing equipment. The proposed laser probe can be divided into two parts, which are Laser-I for laser cutting, and Laser-II for fault injection. We study the advantages of existing equipment, and consider the significant parameters such as energy, repetition rate, wavelength, etc. In this approach, it solves the high energy problem caused by flash-pumping laser in higher repetition frequency from the fiber laser.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1321-1327
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• 2009

In this paper, as the mobile communication service is widely used and the demand for wireless repeaters is rapidly increasing because of the easiness of extending service areas. But a wireless repeater has a problem the oscillation due to feedback signal. We proposed a new hybrid interference canceller using the adaptive filter with CMA(Constant Modulus Algorithm)-Grouped LMS(Least Mean Square) algorithm in the adaptive interference canceller. The proposed interference canceller has better channel adaptive performance and a lower MSE(Mean Square Error) than conventional structure because it uses the cancellation method of Grouped LMS algorithm. The proposed detector uses the LMS algorithms with two different step size to reduce mean square error and to obtain fast convergence. This structure reduces the number of iterations for the same MSE performance and hardware complexity compared to conventional nonlinear interference canceller.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.1976-1979
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• 2008

Studies on binary adder have been variously developed. According to those studies of critical worst delay and mean delay time of asynchronous binary adders, carry select adders (CSA) based on hybrid structure showed 17% better performance than ripple carry adders (RCA) in 32 bit asynchronous processors, and 23% better than in 64 bit microprocessor implemented. In the complicated signal processing systems such as RSA, it is essential to optimize the performance of binary adders which play fundamental roles. The researches which have been studied so far were subject mostly to addition algorithms or adder structures. In this study, we analyzed and designed adders in an asp;ect of synthesis method. We divided the ways of implementing adders into groups, each of which was synthesized with different synthesis options. Also, we analyzed the variously implemented adders to evaluate the performance and area so that we can propose a different approach of designing optimal binary adders.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.421-430
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• 2003

The extraction technique for Doppler information(Doppler centroid frequency(f$_{dc}$) and it's rate(f$_{r}$) is very important to make an image from the radar echo signal data. Clutterlock and auto-focusing techniques have been widely used to extract accurate Doppler information. But both techniques are not easy to implement in SAR processor and need quite lots of time to calculate accurate f$_{dc}$ and f$_{r}$ because they are generally based on echo signal data only. In this paper we suggest hybrid method for Doppler extraction using both of echo signal data and orbital and attitude information of satellite. In this method CDE(Correlation Doppler Estimation) technique is only used to estimate exact modular f$_{dc}$ using received echo signal data and rest of other algorithms are based on simple mathematical model of geometry between satellite and ground targets as well as the Doppler frequency ambiguity resolving problem. The experimental results using Radarsat-1 signal data shows that the proposed method can be effectively used for the extraction of Doppler information.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.393-396
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• 2009

This paper describes a design of variable-length FFT/IFFT processor for OFDM-based communication systems. The designed FFT/IFFT processor adopts the in-place single-memory architecture, and uses a hybrid structure of radix-4 and radix-2 DIF algorithms to accommodate FFT lengths of $N=64{\times}2^k$ ($0{\leq}k{\leq}7$). To achieve both memory size reduction and the improved SQNR, a two-step conditional scaling technique is devised, which conditionally scales the intermediate results of each computational stage. The performance analysis results show that the average SQNR's of 64~8,192-point FFT's are over 60-dB. The processor synthesized with a $0.35-{\mu}m$ CMOS cell library can operate with 75-MHz@3.3-V clock, and 64-point and 8,192-point FFT's can be computed in $2.55-{\mu}s$ and $762.7-{\mu}s$, respectively, thus it satisfies the specifications of wireless LAN, DMB, and DVB systems.

• Structural Engineering and Mechanics
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• v.78 no.5
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• pp.599-610
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• 2021

Early detection of small concrete crack or reinforcement corrosion is necessary for Structural Health Monitoring (SHM). Global vibration based methods are advantageous over local methods because of simple equipment installation and cost efficiency. Among vibration based techniques, FRF based methods are preferred over modal based methods. In this study, a new coupled method using frequency response function (FRF) and proper orthogonal modes (POM) is proposed by using the dynamic characteristic of a damaged beam. For the numerical simulation, wave finite element (WFE), coupled with traditional finite element (FE) method is used for effectively incorporating the damage related information and faster computation. As reported in literature, hybrid combination of wave function based wave finite element method and shape function based finite element method can addresses the mid frequency modelling difficulty as it utilises the advantages of both the methods. It also reduces the dynamic matrix dimension. The algorithms are implemented on a three-dimensional reinforced concrete beam. Damage is modelled and studied for two scenarios, i.e., crack in concrete and rebar corrosion. Single and multiple damage locations with different damage length are also considered. The proposed methodology is found to be very sensitive to both single- and multiple- damage while being computationally efficient at the same time. It is observed that the detection of damage due to corrosion is more challenging than that of concrete crack. The similarity index obtained from the damage parameters shows that it can be a very effective indicator for appropriately indicating initiation of damage in concrete structure in the form of spread corrosion or invisible crack.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.193-202
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• 2022

The classification of hyperspectral imagery (HSI) is essential in the surface of earth observation. Due to the continuous large number of bands, HSI data provide rich information about the object of study; however, it suffers from the curse of dimensionality. Dimensionality reduction is an essential aspect of Machine learning classification. The algorithms based on feature extraction can overcome the data dimensionality issue, thereby allowing the classifiers to utilize comprehensive models to reduce computational costs. This paper assesses and compares two HSI classification techniques. The first is based on the Joint Spatial-Spectral Stacked Autoencoder (JSSSA) method, the second is based on a shallow Artificial Neural Network (SNN), and the third is used the SVM model. The performance of the JSSSA technique is better than the SNN classification technique based on the overall accuracy and Kappa coefficient values. We observed that the JSSSA based method surpasses the SNN technique with an overall accuracy of 96.13% and Kappa coefficient value of 0.95. SNN also achieved a good accuracy of 92.40% and a Kappa coefficient value of 0.90, and SVM achieved an accuracy of 82.87%. The current study suggests that both JSSSA and SNN based techniques prove to be efficient methods for hyperspectral classification of snow features. This work classified the labeled/ground-truth datasets of snow in multiple classes. The labeled/ground-truth data can be valuable for applying deep neural networks such as CNN, hybrid CNN, RNN for glaciology, and snow-related hazard applications.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.489-502
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• 2022

In this study, a Gaussian process regression (GPR) model as well as six GPR-based metaheuristic optimization models, including GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, and GPR-SSO, were developed to predict fly-rock distance in the blasting operation of open pit mines. These models included GPR-SCA, GPR-SSO, GPR-MVO, and GPR. In the models that were obtained from the Soungun copper mine in Iran, a total of 300 datasets were used. These datasets included six input parameters and one output parameter (fly-rock). In order to conduct the assessment of the prediction outcomes, many statistical evaluation indices were used. In the end, it was determined that the performance prediction of the ML models to predict the fly-rock from high to low is GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, GPR-SSO, and GPR with ranking scores of 66, 60, 54, 46, 43, 38, and 30 (for 5-fold method), respectively. These scores correspond in conclusion, the GPR-PSO model generated the most accurate findings, hence it was suggested that this model be used to forecast the fly-rock. In addition, the mutual information test, also known as MIT, was used in order to investigate the influence that each input parameter had on the fly-rock. In the end, it was determined that the stemming (T) parameter was the most effective of all the parameters on the fly-rock.