• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.558-579
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• 2021

The cloud storage provides flexible data storage services for data owners to remotely outsource their data, and reduces data storage operations and management costs for data owners. These outsourced data bring data security concerns to the data owner due to malicious deletion or corruption by the cloud service provider. Data integrity verification is an important way to check outsourced data integrity. However, the existing data verification schemes only consider the case that a verifier launches multiple data verification challenges, and neglect the verification overhead of multiple data verification challenges launched by multiple verifiers at a similar time. In this case, the duplicate data in multiple challenges are verified repeatedly so that verification resources are consumed in vain. We propose a duplicate data verification algorithm based on multiple verifiers and multiple challenges to reduce the verification overhead. The algorithm dynamically schedules the multiple verifiers' challenges based on verification time and the frequent itemsets of duplicate verification data in challenge sets by applying FP-Growth algorithm, and computes the batch proofs of frequent itemsets. Then the challenges are split into two parts, i.e., duplicate data and unique data according to the results of data extraction. Finally, the proofs of duplicate data and unique data are computed and combined to generate a complete proof of every original challenge. Theoretical analysis and experiment evaluation show that the algorithm reduces the verification cost and ensures the correctness of the data integrity verification by flexible batch data verification.

• Journal of the Semiconductor & Display Technology
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• v.16 no.2
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• pp.1-8
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• 2017

The visual inspection of electronic parts consists of two steps: automatic visual inspection and verification inspection. In the stage of a verification inspection, the human inspector sequentially inspects all the areas which detected in the automatic inspection. In this study, we propose an algorithm to determine the order of verification inspection by Bayes inference well known in the field of machine learning. This is a method of prioritizing a region estimated to have a high probability of defect using experience data of past inspection. This algorithm was applied to the visual inspection of ultraviolet filters to verify its effectiveness. As a result of the comparison experiment, it was confirmed that the verification inspection can be completed 30% of the conventional method by adapting proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.1-17
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• 2017

Mobile Cloud Computing has become a promising computing platform. It moves users' data to the centralized large data centers for users' mobile devices to conveniently access. Since the data storage service may not be fully trusted, many public verification algorithms are proposed to check the data integrity. However, these algorithms hardly consider the huge computational burden for the verifiers with resource-constrained mobile devices to execute the verification tasks. We propose an energy-efficient algorithm for assigning verification tasks (EEAVT) to optimize the energy consumption and assign the verification tasks by elastic and customizable ways. The algorithm prioritizes verification tasks according to the expected finish time of the verification, and assigns the number of checked blocks referring to devices' residual energy and available operation time. Theoretical analysis and experiment evaluation show that our algorithm not only shortens the verification finish time, but also decreases energy consumption, thus improving the efficiency and reliability of the verification.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.428-433
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• 1993

In this paper, the fast algorithm to calculate cutting force of milling and its application to NC verification system have been studied. The fast force algorithm can calculate the maximum cutting force fastly during one revelotion of tool. The NC verification using the fast force algorithm can verify excessive cutting force which is the cause of deflection and breakage of tool, and can so adjust the feed rate as to manufacture with the maximum force criterion or maximum machining error criterion. So, the fast force algorithm has been added to the NC verification system, the NC verification system can verify the physical problems in NC code effectively.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.2
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• pp.69-77
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• 2002

Speaker verification can be classified in two categories, text-dependent speaker verification and text-independent speaker verification. In this paper, we discuss text-dependent speaker verification. Text-dependent speaker verification system determines whether the sound characteristics of the speaker are equal to those of the specific person or not. In this paper we obtain the speaker data using a sound card in various noisy conditions, apply a new Hidden LMS (Least Mean Square) adaptive algorithm to it, and extract LPC (Linear Predictive Coding)-cepstrum coefficients as feature vectors. Finally, we use a competitive learning neural network for speaker verification. The proposed hidden LMS adaptive filter using a neural network reduces noise and enhances features in various noisy conditions. We construct a separate neural network for each speaker, which makes it unnecessary to train the whole network for a new added speaker and makes the system expansion easy. We experimentally prove that the proposed method improves the speaker verification performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1334-1339
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• 2018

Viola & Jones's face detection algorithm is a typical face detection algorithm and shows excellent face detection performance. However, the Viola & Jones's algorithm in images including many faces generates undetected faces and wrong detected faces, such as false faces and duplicate detected faces, due to face diversity. This paper proposes an improved face detection algorithm using a face verification algorithm that eliminates the false detected faces generated from the Viola & Jones's algorithm. The proposed face verification algorithm verifies whether the detected face is valid by evaluating its size, its skin color in the designated area, its edges generated from eyes and mouth, and its duplicate detection. In the face verification experiment of 658 face images detected by the Viola & Jones's algorithm, the proposed face verification algorithm shows that all the face images created in the real person are verified.

• The Journal of the Acoustical Society of Korea
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• v.21 no.2
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• pp.190-198
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• 2002

In general, we have used the Viterbi algorithm of Speech recognition for decoding. But a decoder in speaker verification has to recognize same word of every speaker differently. In this paper, we propose a noble decoding algorithm that could replace the typical Viterbi algorithm for the speaker verification system. We utilize for the proposed algorithm the speaker adaptation algorithms that transform feature vectors into the region of the client' characteristics in the speech recognition. There are many adaptation algorithms, but we take MLLR (Maximum Likelihood Linear Regression) and MAP (Maximum A-Posterior) adaptation algorithms for proposed algorithm. We could achieve improvement of performance about 30% of EER (Equal Error Rate) using proposed algorithm instead of the typical Viterbi algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.565-586
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• 2022

Cloud storage's elastic expansibility not only provides flexible services for data owners to store their data remotely, but also reduces storage operation and management costs of their data sharing. The data outsourced remotely in the storage space of cloud service provider also brings data security concerns about data integrity. Data integrity verification has become an important technology for detecting the integrity of remote shared data. However, users without data access rights to verify the data integrity will cause unnecessary overhead to data owner and cloud service provider. Especially malicious users who constantly launch data integrity verification will greatly waste service resources. Since data owner is a consumer purchasing cloud services, he needs to bear both the cost of data storage and that of data verification. This paper proposes a verification control algorithm in data integrity verification for remotely outsourced data. It designs an attribute-based encryption verification control algorithm for multiple verifiers. Moreover, data owner and cloud service provider construct a common access structure together and generate a verification sentinel to verify the authority of verifiers according to the access structure. Finally, since cloud service provider cannot know the access structure and the sentry generation operation, it can only authenticate verifiers with satisfying access policy to verify the data integrity for the corresponding outsourced data. Theoretical analysis and experimental results show that the proposed algorithm achieves fine-grained access control to multiple verifiers for the data integrity verification.

• ETRI Journal
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• v.25 no.3
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• pp.179-186
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• 2003

Using biometrics to verify a person's identity has several advantages over the present practices of personal identification numbers (PINs) and passwords. At the same time, improvements in VLSI technology have recently led to the introduction of smart cards with 32-bit RISC processors. To gain maximum security in verification systems using biometrics, verification as well as storage of the biometric pattern must be done in the smart card. However, because of the limited resources (processing power and memory space) of the smart card, integrating biometrics into it is still an open challenge. In this paper, we propose a fingerprint verification algorithm using a multi-resolution accumulator array that can be executed in restricted environments such as the smart card. We first evaluate both the number of instructions executed and the memory requirement for each step of a typical fingerprint verification algorithm. We then develop a memory-efficient algorithm for the most memory-consuming step (alignment) using a multi-resolution accumulator array. Our experimental results show that the proposed algorithm can reduce the required memory space by a factor of 40 and can be executed in real time in resource-constrained environments without significantly degrading accuracy.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3700-3708
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• 2023

The stochastic origin ensembles method with resolution recovery (SOE-RR) has been proposed to reconstruct proton-induced prompt gammas (PGs), and the reconstructed PG image was used for range verification. However, due to low detection efficiency, the number of valid events is low. Such a low-count condition can degrade the accuracy of the SOE-RR method for proton range verification. In this study, we proposed two strategies to improve the reconstruction of the SOE-RR algorithm for low-count PG imaging. We also studied the number of iterations and repetitions required to achieve reliable range verification. We simulated a proton beam (10⁸ protons) irradiated on a water phantom and used a two-layer Compton camera to detect 4.44-MeV PGs. Our simulated results show that combining the SOE-RR algorithm with restricted volume (SOE-RR-RV) can reduce the error of the estimation of the Bragg peak position from 5.0 mm to 2.5 mm. We also found that the SOE-RR-RV algorithm initialized using a back-projection image could improve the convergence rate while maintaining accurate range verification. Finally, we observed that the improved SOE-RR algorithm set for 60,000 iterations and 25 repetitions could provide reliable PG images. Based on the proposed reconstruction strategies, the SOE-RR algorithm has the potential to achieve a positioning error of 2.5 mm for low-count PG imaging.