• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.233-247
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• 2008

Process monitoring technology is able to detect the faults and the process changes which occur in a process unpredictably, which makes it possible to find the reasons of the faults and get rid of them, resulting in a stable process operation, high-quality product. Statistical process monitoring method based on data set has a main merit to be a tool which can easily supervise a process with the statistics and can be used in the analysis of process data if a high quality of data is given. Because a real process has the inherent characteristics of nonlinearity, non-Gaussianity, multiple operation modes, sensor faults and process changes, however, the conventional multivariate statistical process monitoring method results in inefficient results, the degradation of the supervision performances, or often unreliable monitoring results. Because the conventional methods are not easy to properly supervise the process due to their disadvantages, several advanced monitoring methods are developed recently. This review introduces the theories and application results of several remarkable monitoring methods, which are a nonlinear monitoring with kernel principle component analysis (KPCA), an adaptive model for process change, a mixture model for multiple operation modes and a sensor fault detection and reconstruction, in order to tackle the weak points of the conventional methods.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.4
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• pp.289-296
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• 2014

The rebar detailing is an important work influencing the final performance and quality of RC structures. But it is one of the most irrational and illogical activity in construction site. Many groups of workers, including main contractors, structural engineers, shop drawers, rebar fabricators, and etc., participate in this activity. A loosely-organized process for this activity is apt to produce a big amount of rebar loss or even degraded structures. A 3-dimensional rebar auto-placing system, called as Rebar Hub, has been designed and implemented in this research. Rebar Hub provides a totally integrated service from 3D structural modeling of buildings to rebar auto-placing considering anchorage, splice, and the length of ordered rebar. In addition, Rebar Hub can recognize the 2D drawing CAD files and then build 3D structural models which are used for the start point of 3D rebar auto-placing. After rebar auto-placing, each members of the 3D structural model have rebar information belonging to them. It means that the rebar information can be used for the afterward works such as quantity-survey, manufacturing and fabrication of rebars. Rebar Hub is showing outstanding performance while applying to practical projects. It has almost five times productivity and reduces the rebar loss up to 3~8% of the initially-surveyed amount of rebar.

• Smart Media Journal
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• v.12 no.1
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• pp.9-16
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• 2023

Recently, a convolutional neural network (CNN) based system is being developed to overcome the limitations of human resources in the apple quality classification of farmhouse. However, since convolutional neural networks receive only images of the same size, preprocessing such as sampling may be required, and in the case of oversampling, information loss of the original image such as image quality degradation and blurring occurs. In this paper, in order to minimize the above problem, to generate a image patch based graph of an original image and propose a random walk-based positional encoding method to apply the graph transformer model. The above method continuously learns the position embedding information of patches which don't have a positional information based on the random walk algorithm, and finds the optimal graph structure by aggregating useful node information through the self-attention technique of graph transformer model. Therefore, it is robust and shows good performance even in a new graph structure of random node order and an arbitrary graph structure according to the location of an object in an image. As a result, when experimented with 5 apple quality datasets, the learning accuracy was higher than other GNN models by a minimum of 1.3% to a maximum of 4.7%, and the number of parameters was 3.59M, which was about 15% less than the 23.52M of the ResNet18 model. Therefore, it shows fast reasoning speed according to the reduction of the amount of computation and proves the effect.

• The KIPS Transactions:PartC
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• v.14C no.2
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• pp.147-154
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• 2007

Network topology in ad hoc networks keeps changing because of node mobility and no limitation in number of nodes. Therefore, the scalability of routing protocol is of great importance, However, table driven protocols such as DSDV have been known to be suitable for relatively small number of nodes and low node mobility, Various protocols like FSR, OLSR, and PCDV have been proposed to resolve scalability problem but vet remain to be proven for their comparative superiority for scalability, In this paper, we compare and amine them by employing various network deployment scenarios as follows: network dimension increase's while keeping node density constant node density increases while keeping network dimension fixed, and the number of sessions increase with the network dimension and the number of nodes fixed. the DSDV protocol showed a low scalability despite that it imposes a low overhead because its convergence speed against topology change is slow, The FSR's performance decreased according to the increase of overhead corresponding to increasing number of nodes, The OLSR with the shortest convergence time among them shows a good scalability, but turned out to be less scalable than the PCDV that uses a clustering because of its relatively high overhead.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.519-528
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• 2012

Voice activity detection (VAD) is generally conducted by extracting features from the acoustic signal and a decision rule. The performance of such VAD algorithms driven by the input acoustic signal highly depends on the acoustic noise. When video signals are available as well, the performance of VAD can be enhanced by using the visual information which is not affected by the acoustic noise. Previous visual VAD algorithms usually use single visual feature to detect the lip activity, such as active appearance models, optical flow or intensity variation. Based on the analysis of the weakness of each feature, we propose to combine intensity change measure and the optical flow in the mouth region, which can compensate for each other's weakness. In order to minimize the computational complexity, we develop simple measures that avoid statistical estimation or modeling. Specifically, the optical flow is the averaged motion vector of some grid regions and the intensity variation is detected by simple thresholding. To extract the mouth region, we propose a simple algorithm which first detects two eyes and uses the profile of intensity to detect the center of mouth. Experiments show that the proposed combination of two simple measures show higher detection rates for the given false positive rate than the methods that use a single feature.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.136-138
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• 2005

무선 브로드캐스트 환경에서는 모바일 클라이언트의 제한된 배터리와 클라이언트에서 서버로의 제한된 상향 대역폭 등의 문제로 기존의 동시성 제어 기법을 그대로 사용할 수 없다. 이런 문제를 해결하고자 많은 동시성 제어 기법들이 연구되어 왔는데, 지금까지 제안된 기법들은 편향된 데이터의 접근 패턴을 반영한 브로드캐스트 환경을 고려하지 못하고 있다. 무선 브로드캐스트 환경에서 서버는 일반적으로 모바일 클라이언트의 접근 패턴을 고려하여 편향된 접근 빈도를 갖는 데이터 아이템을 브로드캐스트 한다. 본 논문에서는 무선 브로드캐스트 환경에서 편향된 데이터 접근 패턴을 고려한 동시성 제어 기법을 제안한다. 제안하는 기법은 브로드캐스트 디스크 모델에서 전체 메이저 브로드캐스트 주기마다. 모바일 트랜잭션을 위한 제어 정보를 보내는 것이 아니라 일정한 마이너 브로드캐스트 주기마다. 제어 정보를 전송한다. 이는 접근 빈도가 놓은 데이터가 갱신된 경우 갱신된 내용을 마이너 그룹마다 반영하므로 읽기 전용 트랜잭션이 접근하는 데이터가 최신 정보임을 보장할 뿐만 아니라 갱신 트랜잭션이 최종 검증을 위해서 상향 통신 대역폭을 이용하는 횟수를 줄이고, 보다. 빠른 재실행을 통해 모바일 트랜잭션의 평균 응답시간을 줄여줄 수 있다. 또한 모바일 트랜잭션의 요청이 편향된 경우, 반복적인 트랜잭션의 중단, 재실행으로 인한 성능 저하를 개선하고자 정적 백오프 기법을 이용하여 모바일 트랜잭션 간 충돌 가능성을 줄여준다. 마지막으로 시뮬레이션을 통해 기존의 기법들에 비해 평균 접근 시간, 상향 통신 대역폭 등의 사용량이 현저히 줄어드는 것을 보임으로써 제안하는 기법의 성능을 검증한다.한 평균 access time을 최소화하는 동시에 클라이언트들의 제한된 에너지 소비를 최소화하는데 목적이 있다. 제안기법에 대한 평가는 수학적 분석을 통해 HIDAF 기법과 기존의 브로드캐스트 기법의 성능을 비교 분석한다.하였으나 사료효율은 증진시켰으며, 후자(사양, 사료)와의 상호작용은 나타나지 않았다. 이상의 결과는 거세비육돈에서 1) androgen과 estrogen은 공히 자발적인 사료섭취와 등지방 침적을 억제하고 IGF-I 분비를 증가시키며, 2) 성선스테로이드호르몬의 이 같은 성장에 미치는 효과의 일부는 IGF-I을 통해 매개될 수도 있을을 시사한다. 약 $70 {\~} 90\%$의 phenoxyethanol이 유상에 존재하였다. 또한, 미생물에 대한 항균력도 phenoxyethanol이 수상에 많이 존재할수록 증가하는 경향을 나타내었다. 따라서, 제형 내 oil tomposition을 변화시킴으로써 phenoxyethanol의 사용량을 줄일 수 있을 뿐만 아니라, 피부 투과를 감소시켜 보다 피부 자극이 적은 저자극 방부시스템 개발이 가능하리라 보여 진다. 첨가하여 제조한 curd yoghurt는 저장성과 관능적인 면에서 우수한 상품적 가치가 인정되는 새로운 기능성 신제품의 개발에 기여할 수 있을 것으로 사료되었다. 여자의 경우 0.8이상이 되어서 심혈관계 질환의 위험 범위에 속하는 수준이었다. 삼두근의 두겹 두께는 남녀 각각 $20.2\pm8.58cm,\;22.2\pm4.40mm$으로 남녀간에 유의한 차이는 없었다. 조사대상자의 식습관 상태는 전체 대상자의 $84.4\%$가 대부분

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.11-18
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• 2014

Most of the energy efficient MAC protocols for wireless sensor networks (WSNs) are based on duty cycling in a single channel and show competitive performances in a small number of traffic flows; however, under concurrent multiple flows, they result in significant performance degradation due to contention and collision. We propose a multi-channel pipelining (MCP) method for convergecast WSN in order to address these problems. In MCP, a staggered dynamic phase shift (SDPS) algorithms devised to minimize end-to-end latency by dynamically staggering wake-up schedule of nodes on a multi-hop path. Also, a phase-locking identification (PLI) algorithm is proposed to optimize energy efficiency. Based on these algorithms, multiple flows can be dynamically pipelined in one of multiple channels and successively handled by sink switched to each channel. We present an analytical model to compute the duty cycle and the latency of MCP and validate the model by simulation. Simulation evaluation shows that our proposal is superior to existing protocols: X-MAC and DPS-MAC in terms of duty cycle, end-to-end latency, delivery ratio, and aggregate throughput.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.2
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• pp.67-74
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• 2010

A variation of temperature acting on a RC roof slab causes a change of stress in concrete since it expands during summer and is compressed during winter. This behavior repeats annually and makes an affection to the structural capacity of member for both serviceability and ultimate level. In this paper, a cyclic temperature loading variation is calculated by analyzing the weather data of Korea for 20 years. In addition, an experimental work is planned to find the long term effect of temperature variation. Six RC slab are made with same dimension. Test parameters are loading duration (10, 20, 30 year) and whether it has pre-damage or not. Observation of stiffness variations according to cyclic loading period shows that the serious stiffness drop happens after 10 year's cyclic loading at summer while after 30 year's loading at winter. From the fracture test about slabs damaged by long term cyclic loading, however, the capacity of member such as initial stiffness and maximum strength were not changed except yield strength according to the period of long term cyclic loading. The yield strength tends to decrease after 20 year's cyclic loading.

• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.31-38
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• 2024

The digital phase-locked loop(DPLL) is one of the circuits composed of a digital detector, digital loop filter, voltage-controlled oscillator, and divider as a fundamental circuit, widely used in many fields such as electrical and circuit fields. A state estimator using various mathematical algorithms is used to improve the performance of a digital phase-locked loop. Traditional state estimators have utilized Kalman filters of infinite impulse response state estimators, and digital phase-locked loops based on infinite impulse response state estimators can cause rapid performance degradation in unexpected situations such as inaccuracies in initial values, model errors, and various disturbances. In this paper, we propose a two-layer Frobenius norm-based finite impulse state estimator to design a new digital phase-locked loop. The proposed state estimator uses the estimated state of the first layer to estimate the state of the first layer with the accumulated measurement value. To verify the robust performance of the new finite impulse response state estimator-based digital phase locked-loop, simulations were performed by comparing it with the infinite impulse response state estimator in situations where noise covariance information was inaccurate.