Earticle

The unprecedented growth of mobile technology has generated an increase in malware and raised concerns over malware threats. Different approaches have been adopted to overcome the malware attacks yet this spread is still increasing. To combat this issue, this study proposes an Android malware detection system based on permission features using Bayesian classification. The permission features were extracted via the static analysis technique. The 10,000 samples for the judgement were obtained from AndroZoo and Drebin databases. The experiment was then conducted using two algorithms for feature selection: information gain and chi-square. The best accuracy rate of detection of permission features achieved was 91.1%.

In this paper, we proposed model for European option pricing using predicted cumulative probability distribution of hidden Markov Model. The Black-Scholes model is based on Gaussian distribution and the model's main shortfall is fat-tail problem. Because of fat-tail problem, the implied volatility surface is shaped skew, smirk or other forms. To correct model's drawback, we extract predicted cumulative distribution using hidden Markov Model and simulate the underlying price paths using Monte Carlo method. In the results, our model re ect current implied volatility surface suciently.

In this paper, we apply a logistic regression model to the data of snow damage on sugi (Cryptomeria japonica) occurred in Toyama prefecture (in Japan) in 2004 for estimating the risk probability. In order to specify the factors effecting snow damage, we apply a model selection procedure determining optimal subset of explanatory variables. In this process we consider the following 3 information criteria, 1) Akaike’'s information criterion, 2) Baysian information criterion, 3) Bias-corrected Akaike’'s information criterion. For the selected variables, we give a proper interpretation from the viewpoint of natural disaster.

몰리브데나이트는 몰리브덴의 주요 광물자원으로 고유의 소수성 표면으로 인해 부유선별을 통해 회수된다. 한편, 채광되는 몰리브데나이트의 결정크기 및 품위가 낮아지고 있다. 이로 인해, 단체분리에 요구되는 광물 크기가 작아짐으로써, 부유선별에 투입되는 원광 크기 또한 미립화되고 있다. 미립자는 기포와의 충돌확률이 낮아, 부유선별할 때 효율 감소를 유발시킨다. 이에 따라, 몰리브덴 확보를 위해서는 미립 몰리브데나이트에 대한 부유선별 연구가 필요한 실정이다. 본 연구에서는 5-30 μm의 미립 몰리브데나이트의 부유선별 효율을향상시킬 수 있는 방안을 제안하였다. 기포크기 축소와 입자응집을 통한 효율 증진으로 접근하였다. 기포-입자 충돌확률을 시뮬레이션과부유선별 실험을 통해, 미립자의 부유선별 효율이 향상될 수 있는 기포크기 및 입자응집체 크기에 대한 범위를 정량적으로 결정하였다. 결과적으로 본 연구에서 제공한 미립 몰리브덴광 부유선별 조건은 향후 몰리브덴 선광 플랜트의 부유선별 공정을 향상시키는 데 활용될예정이다.

Molybdenite is the primary molybdenum resource and is extracted via flotation due to its unique hydrophobic surface. Meanwhile, the grade and crystal size of mined molybdenite are decreasing. As a result, the size of the molybdenum ore required for liberation is decreasing, and the flotation process's feed size input is also decreasing. Therefore, in order to secure molybdenum, it is necessary to perform research on the flotation for the fine molybdenite. In this study, we developed a method to enhance the flotation efficiency of fine molybdenite particles in the range of 5?30 μm. The methodology involved implementing bubble size reduction and particle aggregation. Through simulations of bubble-particle collision probability and flotation experiments, we were able to find the ranges of bubble size and particle aggregate size that make fine particles float more effectively. This range provided the conditions for effective flotation of fine molybdenite particles. Therefore, we will implement the flotation conditions established in this study for fine molybdenum ore to improve the flotation process in molybdenum mineral processing plants in the future.

차량이동통신망의 가장 중요한 서비스 중의 하나인 안전운전 및 사고방지는 브로드캐스트의 신뢰적 전송에 크게 좌우된다. 브로드캐스트의 신뢰적 전송과 관련된 기존 연구들은 네트워크 밀집 상황에 따라 경쟁윈도우(Contention Window)의 크기를 휴리스틱하게 조절하는 방법을 통해 신뢰적 전송이 가능하도록 제안하고 있으나, 이러한 방법들은 여러 가지 문제점들을 가지고 있다. 본 논문에서는 경쟁 노드들의 수와 경쟁윈도우의 크기에 따른 싱글홉 브로드캐스트 전송 실패 확률을 계산할 수 있는 확률모델을 제안한다. 이 모델을 통해 경쟁노드의 수에 따른 브로드캐스트 전송 실패 확률을 최소로 하는 경쟁윈도우의 최소 크기를 계산하고, 브로드캐스트 시 경쟁윈도우의 크기로 사용함으로써, 가능한 최소의 전송딜레이를 가지는 동시에 브로드캐스트 전송 신뢰도를 크게 향상시킨다. 이 모델의 정확도 및 싱글홉, 멀티홉 브로드캐스트 전송의 신뢰도를 다양한 네트워크 밀도 시나리오에 따른 시뮬레이션을 통해검증하였고, 거의 100 퍼센트에 가까운 신뢰적 전송 결과를 보여준다.

The reliability of broadcasting over vehicular ad hoc networks (VANETs) is one of the most critical factors for driving safety applications. There exists limitations to improve the reliability of broadcast transmissions in saturated VANETs where previous proposals in literature tackle the problem by heuristically adapting the size of the contention window (CW). This paper considers improving the reliability by proposing a new probability model based on the counting methods of permutations and combinations, which counts all the possible cases of broadcast failures in a single-hop broadcast transmission for a given CW. From the model, we calculate the best CW size given the number of contention nodes, which significantly improves the reliability and satisfying the timely dissemination of emergency broadcasting messages. Through extensive VANET simulations with varying densities, we show that our model maintains near 100 percent success rate for single-hop broadcast as well as multi-hop broadcast (e.g. 40 hops) and achieves minimal broadcast delay.

휴게소는 장거리 운전자나 졸음 운전자가 충분히 쉴 수 있는 공간을 조성하여 사고를 미 연에 방지하는 중요한 역할을 한다. 따라서 휴게소의 적절한 위치 선정은 필수적이며, 이를 위해 정확한 수요 예측과 이용자 통행 행태를 분석하는 것은 매우 중요하다. 따라서 본 연구 는 고속도로에서 RSE를 통해 수집되는 DSRC자료를 이용하여 안성휴게소(상행)를 이용하는 휴게소 이용자의 통행 행태를 분석하였으며, 통행행태지표를 토대로 휴게소 이용 확률 모형 을 개발하고 검증하였다. 분석결과, 안성 휴게소(상행) 이용 행태는 통행시간이 평일 90분 이상, 주말 70분 이상일 때 가장 이용 빈도가 높았으며, 휴게소부터 통행종료까지 남은 거리가 30km 이하일 때, 휴게 소 이용률은 급격히 감소하는 것으로 분석되었다. 본 연구의 휴게소 이용 확률 모형을 통해 추정된 휴게소 이용률은 실이용률과 1~2%오차가 발생했다. 본 연구 결과는 정형화된 자료를 이용하여 휴게소 이용 행태를 분석한데 의의가 있으며, 본 연구의 휴게소 이용차량 분별방법론과 개별휴게소이용확률 모형 개발방법은 향후 휴게소 입지선정과 이용자의 서비스 수준 향상을 위한 차별화 전략에 적극 활용될 수 있을 것으로 판단된다.

A Service Area plays an important role in preventing accidents in advance by creating a space for long distance drivers or drowsy drivers to rest. Therefore, proper positioning of the expressway service area is essential, and it is important to analyze accurate demand forecasting and user travel behavior. Thus, this study analysis travel behavior and developed odel of the probability of using the service area by using the DSRC data collected by the RSE on the highway. According to the analysis, the usage behavior of highway service areas was most frequently when travel time was 90 minutes or more on weekdays and 70 minutes or more on weekends. The utilization rate of the service area estimated from the probability model of use of the rest area in this study was 1 % to 2 % error. The results of this study are meaningful in analyzing the behavior of the use of rest areas using the structured data and can be used as a differentiated strategy for selecting the location of rest areas and enhancing the service level of users.

One of the important factors of technology development strategies is the expected economic value of a technology. As the focus of national defense policy have been turned from domestic consumption to international consumption, a more accurate technology valuation model is required for defense industries. This research presents a new model for evaluating economic value of technologies by the modified process which adopted a Markov Chain and a Risk-averse Utility Function. The major accomplishment of this research is the designing of the process which estimates the technology market lifespan based on the probability data. It also enables us to consider the expected events. Moreover, a new technology valuation model is designed to reflect the characteristics of defense industries at the specific technique level.

최근 LTE보다 빠른 속도와 안정을 가진 5G 기술에 대한 기대감이 증가하고 있는 가운데 5G 통신 보안에 대한 관심이 증가하고 있다. 그러나, 5G는 현재까지 이질적인 영역이 서로 포함되어 있어서 보안 영역에 대한 문제들을 아직 완벽하게 지원하고 있지 않다. 본 논문은 5G 환경에서 IoT 장치의 인증을 블록체인에 적용한 확률 기반의 IoT 관리 모델을 제안한다. 제안 모델은 IoT 장치의 인증을 확률적 이론과 물리적 구조를 효율적으로 융합하기 위해서 n 계층의 IoT 사용자를 n + 1 계층과 n - 1 계층의 관리자가 쌍방향 인증이 이루어지도록 2개의 랜덤키를 역으로 사용한다. 제안 모델은 5G 환경의 IoT 사용자에 대한 인증을 확률적 기반으로 IoT 정보를 계층화시킨 후 IoT 정보를 가중치에 적용하여 그룹핑된 IoT 정보를 블록체인으로 연결한다. 또한, 제안 모델은 5G 네트워크를 계층화된 다층 네트워크로 분할하기 때문에 기존 블록체인보다 향상된 기능을 가진다.

Interest in 5G communication security has been growing recently amid growing expectations for 5G technology with faster speed and stability than LTE. However, 5G has so far included disparate areas, so it has not yet fully supported the issues of security. This paper proposes a blockchain-based IoT management model in order to efficiently provide the authentication of users using IoT in 5G In order to efficiently fuse the authentication of IoT users with probabilistic theory and physical structure, the proposed model uses two random keys in reverse direction at different layers so that two-way authentication is achieved by the managers of layers and layers. The proposed model applied blockchain between grouped IoT devices by assigning weights to layer information of IoT information after certification of IoT users in 5G environment is stratified on a probabilistic basis. In particular, the proposed model has better functions than the existing blockchain because it divides the IoT network into layered, multi-layered networks.

최근 IoT 네트워크는 이기종의 IoT 장치에서 발생하는 데이터를 효율적으로 처리하기 위해서 다양한 클러스터 링 기법들이 연구되고 있다. 그러나, 기존 클러스터링 기법들은 정적으로 네트워크를 분할하는데 초점을 맞추고 있어서 이동이 가능한 IoT 장치에는 기존 클러스터링 기법들이 적합하지 않다. 본 논문에서는 에지 네트워크를 이용하여 IoT 장치의 정보를 수집⋅분석하기 위한 확률적 딥러닝 기반의 동적 클러스터링 모델을 제안한다. 제안 모델은 수집된 정보 의 속성값의 빈도수를 확률적으로 딥러닝에 적용하여 서브넷을 구축한다. 구축된 서브넷은 시드로 추출된 연계 정보를 계층적 구조로 그룹핑할 때 사용하며, IoT 장치에 대한 동적 클러스터링의 속도 및 정확도를 향상시킨다. 성능평가 결 과, 제안모델은 기존 모델에 비해 데이터 처리 시간이 평균 13.8% 향상되었고, 서버의 오버헤드는 기존 모델보다 평균 10.5% 낮게 나타났다. 서버에서 IoT 정보를 추출할 때의 정확도는 기존모델보다 평균 8.7% 향상되었다.

Recently, various clustering techniques have been studied to efficiently handle data generated by heterogeneous IoT devices. However, existing clustering techniques are not suitable for mobile IoT devices because they focus on statically dividing networks. This paper proposes a probabilistic deep learning-based dynamic clustering model for collecting and analyzing information on IoT devices using edge networks. The proposed model establishes a subnet by applying the frequency of the attribute values collected probabilistically to deep learning. The established subnets are used to group information extracted from seeds into hierarchical structures and improve the speed and accuracy of dynamic clustering for IoT devices. The performance evaluation results showed that the proposed model had an average 13.8 percent improvement in data processing time compared to the existing model, and the server's overhead was 10.5 percent lower on average than the existing model. The accuracy of extracting IoT information from servers has improved by 8.7% on average from previous models.

Making a contract of small private construction works is very often disregarded important or subsidiary contract details which strongly recommended in standard contract sheet. To have a well fulfilled contract sheet with detailed agreements are very symbolic to promising their rights and obligations to each other until they meet the final objectives of completion, delivery, and receipt of construction payments. Therefore, a almost perfect contract give us less disputes for payments during construction or after completion of work. In this study, a logistic regression model was used to determine the effect of subcontract items from survey. The results show that contract sheet, design alternation and cancellation of contract are the most important contract conditions, and derived a logistic regression model for reducing a dispute between both personal contract.

최근 국내 기관 투자자들을 중심으로 전통적 투자대상으로부터의 수익이 하락추세에 있어 기관 투자자들이 적극적 자산운용을 기피할 경우, 장기적으로 안정적 수익보장을 유지하기 어렵다는 우려가 제기되었다. 이에 보유자산 구성을 조정한 수익성 개선전략의 요구가 증대되고 있으며, 일부 기관 투자자들은 헤지펀드를 기존 포트폴리오에 편입시킴으로써 운용수익률을 제고하려는 움직임을 보이고 있다. 본 연구에서는 시스템트레이딩을 이용하여 선물시장에서 거래확률 조정을 통한 헤지펀드 투자전략을 제시하고자 한다. 선물시장에서 사용되는 다양한 기술적 지표를 이용하여 연관성 규칙(association rule)을 생성하고 이를 거래규칙(trading rule)으로 전환하여 투자전략으로 활용한다. 한편 연관성 규칙은 기술적 지표의 개수와 개별 지표들의 구간값의 조합으로 생성되며, 조합에 따라 거래확률을 조정함으로써 위험관리가 가능한 투자전략을 수립하는데 사용된다. 제시된 전략의 우수성을 입증하기 위해 KOSPI 200 연결선물데이터를 이용하여 수익성 분석을 수행하였으며, 분석결과 제시된 투자전략이 투자위험관리에 효과적임을 보였다.

This paper attempts to offer an effective strategy of hedge fund based on trade probability control in the futures market. By using various technical indicators, we create an association rule and transforms it into a trading rule to be used as an investment strategy. Association rules are made by the combination of various technical indicators and the range of individual indicator value. Adjustments of trade probabilities are performed by depending on the rule combinations and it can be utilized to establish an effective investment strategy onto the risk management. In order to demonstrate the superiority of the investment strategy proposed, we analyzed a profitability using the futures index based on KOSPI200. Experiments results show that our proposed strategy could effectively manage and response the dynamics investment risks.

사전 확률분포를 모델링하는 HMM을 사용하는 어휘 인식에서 인식 어휘의 모델들의 대한 인식 확률이 이 산적인 분포를 나타내며 인식을 위한 계산량이 적은 장점이 있지만 인식률을 계산했을 때 상대적으로 낮은 단점이 있다. 이를 개선하기 위하여 베이시안 기법 어휘 인식 모델을 융합한 잡음 제거 인식률 향상을 제안한다. 본 논문은 베이시안 기법 어휘 인식을 위한 모델 구성을 베이시안 기법의 최적화한 인식 모델을 구성하였다. HMM을 기반으로 한 사전 확률 방법과 베이시안 기법인 사후확률을 융합하여 잡음을 제거하고 인식률을 향상시켰다. 본 논문에서 제 안한 방법을 적용한 결과 어휘 인식률에서 98.1%의 인식률을 나타내었다.

In vocabulary recognition using an HMM model which models the prior distribution for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. The Bayesian techniques to improve vocabulary recognition model, it is proposed using a convergence of two methods to improve recognition noise-canceling recognition. In this paper, using a convergence of the prior probability method and techniques of Bayesian posterior probability based on HMM remove noise and improves the recognition rate. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.

This paper tries to analyze how effective teaching model through Freudenthal’s Mathematization in Probability and statistics is for students' enhancement of mathematization, mathematics scores and mathematical interest. We made two groups, of high school students, one is the experiment group and, the other is the comparable group. The comparable group was taught by traditional instructional method, on the other hand, the experimental group was taught by instructional method using Freudenthal’s Mathematization for two months. From the result of the study, we became to know that students who were taught through Freudenthal’s Mathematization will be able to get effects as follows. First, we see in this study that instructional method through Freudenthal’s Mathematization improves mathematization of the experiment group and it largely enhances mathematics scores of them. Second, we show in this research that the mathematization improves their mathematical confidence as well as their mathematical interest.

본 연구는 대한민국 정부의 기술혁신형 국가연구개발사업 중 하나인 “지식경제기술혁신사 업”을 대상으로 R&D 성과창출 성공확률의 예측모형을 추정하고, 추정된 예측모형의 민감도 검토를 위한 실험설계분석을 실시한다. 분석 대상 R&D 주관기관 유형은 “대기업”, “중소기 업”, 두 가지 영리기관이며, “국가R&D사업관리서비스”에 매년 등록 관리되는 여섯 가지 핵 심 R&D 성과 항목 중 특히, 특허등록, 매출액, 두 가지를 분석 대상으로 선정한다. 또한, 타 기관 간 R&D 협력 여부도 영향요인으로 본 연구에 포함한다. 일반적인 R&D 논리모형에 기 초하여 예측모형을 수립하며, 분석 샘플 n = 1,929개 종료 R&D 과제를 대상으로 분석 샘플 특성을 고려한 로짓 (Logit) 회귀분석을 실시한다. 실증분석 결과를 요약하면, 첫째, 정부지원 금액 대비 성과창출 성공확률의 양 (+)의 상관성은 확인되나, 그 영향력은 특허등록과 같은 단기적, 기술적 산출에서 매출액과 같은 중기적, 경제적 결과로 진행될수록 감소하였다. 둘째, 매출액 성과창출 여부는 직전 단계에 위치한 특허등록 창출 여부에 의존적임이 확인되었다. 셋째, “단독 R&D”와 비교할 때 “협력 R&D”에서 특허등록, 매출액 성과창출 성공확률이 더 높고, “대기업”보다 “중소기업”의 매출액 성과창출 성공확률이 더 높았다. 넷째, 민감도 실험 설계분석을 통해, 정부지원금액이 증가할수록 특허등록 성과창출 성공확률은 S자형 비선형 성장곡선 형태의 증가 추세를 명확히 보인 반면, 매출액 성과창출 성공확률은 상대적으로 선 형에 근사한 둔감한 증가 추세를 보였다. 본 연구가 제안하는 정책적 함의로서, 각 R&D 과 제별 순차적인 성과창출 유도, 타 기관 간 R&D 협력 활성화, 중소기업 친화적인 국가연구개 발사업의 기획 전개 관리 등이 바람직하다고 판단된다. 또한, 정부지원금액 증액을 통해 성 취 가능한 R&D 성과 항목은 한정적일 수 있으므로, 각 R&D 과제별 정부지원금액 규모의 적절성을 세밀히 검토하는 절차가 향후 좀 더 강화될 필요가 있다고 판단된다.

This study presents an empirical case where a prediction model is estimated for the performance creation success probability regarding a national technology innovation research and development (R&D) program. Additionally, a framework for a design of experiments is established to conduct sensitivity analyses on the predicted probability. The prediction model is developed based on typical R&D logic models, and it encompasses the total of five distinct R&D characteristics such as R&D Government Subsidy (X1), Patent Registration (Y1), Sales (Y2), Company Type (T1) and Collaboration Type (T2). In the analysis, the sample of n = 1,929 observations (i.e., R&D projects) is analyzed using Logit regression models to cope with the sample’s nonparametric characteristics. In particular, an interesting finding from this study is that the predicted probability for Patent Registration (Y1) performance creation success shows an S-shape nonlinear growth curve trend as R&D Government Subsidy (X1) increases. As practical implications, we suggest the following; (1) promoting the sequential R&D performance creation, (2) enabling the R&D collaboration between other agencies, (3) planning, deployment and management for SME-friendly national R&D programs. In addition, a meticulous review process should be incorporated into the R&D management scheme because it was found that the increased amount of R&D Government Subsidy (X1) might have a limited effect up to the short term and technical outputs only.

With the development of cloud computing technology, people not only want to pursue the shortest time to complete the tasks by using cloud computing, but also hope to take into the running costs of machines. Existing task scheduling algorithm in the cloud computing environment has been unable to meet people's needs. As an extension and generalization of the model checking theory, probability model checking is also used in many fields, such as random distributed algorithm and other areas. The task scheduling algorithm based on the particle swarm optimization algorithm combined with probability model is proposed in this paper. The algorithm defines the fitness functions of the time cost and the running cost. The fitness functions can improve the efficiency of the cloud computing platform. At the same time, the probability model can be used to analyze the running states of machines and the computing capability of the nodes in the cloud cluster. The probability, which is calculated by the probability model, provides the basis for changing particle swarm algorithm’s the inertia factor and the learning factor, so as to solve the drawback that the inertia factor and the learning factor solely depend on the fixed value.

Energy limited become a key and hot point problems of wireless sensor networks. This paper proposes a Coverage Strategy Based on probability-aware Model in Wireless Sensor Networks. The strategy using the probability-aware Model, Combining with the node coverage situation, eliminate redundant nodes, establish the optimal work node set, designed to reduce the network energy consumption, set a reasonable number of working nodes. The simulation results show that the new strategy not only to improve the network coverage, but also effectively prolong the network lifetime, improve the quality of network ,Meanwhile network coverage optimization control is realized.

This study is devoted to overcome the underflow problem and poorly cost-effective limitation of model-set adaptive IMM algorithm. Cause of underflow problem in Novel-IMM is addressed firstly, based on which an underflow prevented selection probabilities (UPSP) algorithm is presented to solve this problem. This paper then presents a fast model-set adaptive (FAIMM) IMM algorithm based on steady state Kalman filters that decrease the computational burden greatly while keeping acceptable tracking accuracy. Finally, the threshold choosing strategy of UPSP algorithm is presented, which could make the FAIMM algorithm achieves ideal performance. Simulation results demonstrate that the FAIMM algorithm can be an effective estimator in real-time application.

This study presents the design and implementation of an educational simulation game that reproduces fire situations in a virtual environment to improve users’ safety response capabilities. The proposed system employs a probability-based cellular partitioning algorithm, which divides indoor spaces into grid-shaped cells and probabilistically determines fire spread based on each cell’s material, temperature, and location. This approach enables the efficient representation of realistic fire spread patterns while maintaining performance suitable for real-time gameplay. The game is set in a 3D graphic environment and played from a first-person perspective, allowing players to experience the entire process from fire outbreak to evacuation using various suppression and evacuation tools such as fire extinguishers and escape ladders. This paper analyzes the structure and learning effects of the proposed system, and discusses both the educational applicability and technical limitations of the probability-based simulation model. Future work will explore enhancing educational effects through the inclusion of more complex fire scenarios, user behavior AI, and VR interface integration.

In order to estimate the temporary-permanent workers’ wage gap which is caused by the difference in contract types across the marginal wage distribution with controlling for individual unobserved heterogeneity, recent studies applied UQR (unconditional quantile regression) with individual FE (fixed-effects). Since, as a dependent variable, UQR uses the RIF (recentered influence function) which has a binary outcome, UQR is a BPM (binary probability model) that has a theoretical interest in the effects on a latent dependent variable which is continuous. Based on the empirical results of Firpo et al. (2009) and the widely held belief that a LPM (linear probability model) well approximates a BPM, subsequent studies have generally used a LPM instead of a BPM. In the case of controlling for individual FE, linear FE regression and logistic FE regression can be used for a LPM and a BPM, respectively. However, these two regressions have a critical difference that individuals having no longitudinal variation in the RIF are excluded in logistic FE regression and not in linear FE regression. In a strict sense, however, individuals having no longitudinal variation in the RIF have to be excluded from the sample because the partial effects of explanatory variables on a latent continuous dependent variable are not non-existent but just not identified in these individuals. By analyzing panel data of South Korea, I find that the inclusion of individuals having no longitudinal variation in the RIF substantially underestimates both the temporary-permanent wage gap and the confidence interval of that, especially at the extreme quantiles. Even so, if the aim of the study is to just use the within-variance and not to strictly control for individual FE based on the binary choice model, it could be possible to include individuals having no longitudinal variation in a dependent variable in the analysis.