본 연구는 혁신 생태계 조성을 통한 경제성장 뿐만 아니라 자기고용을 통한 실업해소, 성장 단계별 적절한 인재 채용을 통한 고용창출 등 작금에 우리가 직 면한 사회·경제적 문제를 해결하기 위한 수단으로 창업이 매력적인 대안이 될 수 있음을 전제하였다. 나아가, 이와 같은 전제를 바탕으로 실질창업 활성화에서 중요한 것은 기회인식임을 제안하였고 기회인식에 영향을 미치는 변수들을 규명 하였다. 구체적으로, 기업가정신이 기회발견행동 및 창업기회인식에 미치는 영향 과 기회발견행동이 창업기회인식에 미치는 영향을 구조방정식 모델을 통해 실증 하였다. 또한 기업가정신과 창업기회인식의 관계에서 기회발견행동의 매개효과를 실증하였다. 연구결과 첫째, 기업가정신은 창업기회인식은 물론 기회발견행동을 구성하는 4개의 하위변수에 모두 통계적으로 매우 유의미한 영향을 미치는 것으 로 나타났다. 둘째, 기회발견행동의 하위 변수 중 관찰과 아이디어 네트워킹은 창업기회인식에 통계적으로 유의미한 영향을 미치는 것으로 나타났다. 셋째, 기 업가정신이 창업기회인식에 미치는 영향에서 아이디어 네트워킹의 매개효과가 검증되었다. 본 연구는 이상의 실증분석 결과를 바탕으로 기업가정신 및 창업교 육 혁신을 중심으로 이론적·실무적 시사점을 제안하였음에 의의가 있다.

자연자원은 인간의 삶과 매우 긴밀한 관계를 가지고 있으며, 식품, 의료, 과학, 산업, 환경 등 다양한 분야에서 중요한 역할을 한다. 그러나 많은 종류의 자연자원은 인간 사회의 급속한 발전으로 인한 환경의 오염으로 멸종 위기에 처해있다. 이러한 이유로 자연자원을 위한 자연영상 인식에 관한 연구는 매우 중요하고 최근에 많은 연구가 진행되고 있다. 자연자원을 이용한 산업규모는 급격하게 신장되고 있으며 이에 따라 경제적 이해관계를 둘러싼 국제적 관심도 급속히 증대하고 있다. 하지만 효과적인 자연자원을 확보하고, 관리에 필수적인 기반 기술로써 자연 영상 인식 방법은 제약적이며, 한정적인 소수의 학제적 연구만 진행 되었을 뿐 상용화 진행은 미미하다. 이에 본 발표에서는 기존 연구 사례를 통해 패턴인식을 이용한 자연인식 기술과 동향을 알아본다.

This study is conducted to investigate to the consumption pattern of Kimchi and perception about the functional Kimchi of consumer. The survey was done between October 1 to October 15, 2011 among 294 male and female adults aged 19 and over in Seoul and Gyeonggi-do areas. The gender distribution of subjects was 33.3% males and 66.7% females. 64.3% of subjects prepared Kimchi by themselves, 23.5% of subjects received Kimchi from relatives and 12.2% of subjects purchased Kimchi from the market. In addition, the rate of preparing Kimchi at home is highest in those aged fifty or over. Only 41.8% of subjects knew how to make Kimchi. 72.1% of subjects responded that they ate Kimchi one or more a day. 46.6% of subjects have purchased commercial Chinese cabbage Kimchi. The amount of one-time purchase of commercial Kimchi were investigated; 45.2% of subjects have been buying 500-1 kg, 34.4% of the subjects bought less than 500 g, and 11.2% of subjects bought 1-3 kg. 28.2% of subjects buy Kimchi at the supermarket and warehouse market. With regard to the evaluation of Kimchi taste, most consumers were not satisfied with the sweetness of Kimchi. In this result, the perception about functional Kimchi was very low. Consumer's demands were as follows: nutrient enhancement, strengthening of biologically active substances, lactic acid bacteria enhancement in order. Small sales units were preferred by the consumers, and complementation of sweetness of kimchi was required. Various Kimchi including functional Kimchi must be developed to meet the needs of consumers.

This study examines the dietary, exercise, and other daily habits of Daegu residents and how these relate to the residents’ perception of their own health status, and comparatively analyzes the lifestyles and daily habits of those who perceive themselves to be healthy and those who do not. This research study used Inbody 230(Body composition analysis, Biospace, Korea) to make body and health measurements such as body composition and obesity index. Also, included in this study was a survey on the lifestyle patterns of the residents. The data from this survey was analyzed with SPSS. The results show that among the residents of Daegu, those who perceive themselves to be healthy have lower body fat and are less obese on average. With respect to dietary habits, those who believe themselves to be healthy have more regular dietary habits such as rarely skipping a meal than those who do not believe themselves to be healthy. In addition those who say they are healthy exercise more frequently and for a longer duration than those who say they are not healthy. As for daily habits, those who report they are healthy show greater satisfaction with life and suffer less from stress than those who report themselves to be unhealthy. This study demonstrates that in order to improve people’s health in Korea, good dietary, exercise and daily habits need to be emphasized. Additionally, health education and health awareness programs need to be established in each region. Follow-up studies should be conducted afterwards.

본 논문에서는 Electrocencephalogram(EEG) 신호를 이용한 BCI(Brain-Computer Interface) 시스템 연구와 PC 게임의 컨 트롤러를 대체 할 수 있는 가능성에 대해서 실험하였다. 국제 전극 부착법에 의한 C4지역 Single Sensor 부착을 통해 측정되어지는 Raw data의 필요 범위 주파수 영역을 밴드 패스 필터링으로 추출하고, 특징 신호를 이용하여 ERS(Event-Related Synchronization)와 ERD(Event-Related Desynchronization) 반응을 측정 한다. 추출된 특징 신호의 평균 값에 FFT를 이용한 파워스펙트럼으로 분석하여 산출된 각 주파수별 분포를 SVM(Support Vector Machine)과 LDA(Linear Discriminant analysis)의 두 가지 선형 분리 방식으로 분류한다. 이와 같이 분류된 서포트 벡터 클래스는 EEG 좌우 방향 인식 패턴을 키보드 입력하는 대신 수치 입력값으로 대체 할 수 있다. 또한, 제안된 EPSVM 알고리즘 과 기존의 알고리즘을 비교하여 개선된 인식률을 증명한다.

게임은 가장 중요한 콘텐츠로 자리매김하였다. 이러한 중요성에 따라, 패턴 인식 기술을 활용한 컴퓨터 게임 제작 사례가 점점 늘고 있다. 이것은 게임 속에서 벌어지는 세계가 점점 복잡해지고 게임의 흥미도에 대한 사용자의 욕구 가 증대하기 때문에 생기는 자연스런 현상이다. 이 논문은 신경망과 HMM을 이용한 사례를 집중적으로 살펴보며, SVM과 결정 트리를 사용한 경우도 언급한다. 최근의 논문에 대해 어떻게 특징을 추출하였는가, 어떤 분류기를 사용하 고 그것의 구조가 어떤지, 그리고 활용한 결과로 얻은 효과에 집중하여 사례를 조사하였다. 토론에서는 향후 전개 방 향과 연구 주제에 대해 언급한다.

The objective of this study is to evaluate the mechanical behaviors and structural integrity of the weldment of high strength steel by using an acoustic emission (AE) techniques. Monotonic simple tension and AE tests were conducted against the 3 kinds of welded specimen. In order to analysis the effectiveness of weldability, joinability and structural integrity, we used K-means clustering method as a unsupervised learning pattern recognition algorithm for obtained multi-variate AE main data sets, such as AE counts, energy, amplitude, hits, risetime, duration, counts to peak and rms signals. Through the experimental results, the effectiveness of the proposed method is discussed.

유아들의 놀이 도구 중 하나인 스토리 북 게임은 책을 펼친 뒤, 해당 내용을 눈으로 보면서 이야기 전개에 따라 해당하는 페이지로 이동하면서 이야기를 엮어가는 게임이다. 스토리 북 게임은 유아들에게 게임과 학습의 기능을 접목시킨 것으로 사고력 증진을 위해서 많이 활용되고 있으나 시각적 효과의 한계와 청각적 효과를 제공하지 못하는 단점을 보완하기 위해서는 멀티미디어 매체와의 접목이 불가피하다. 하지만 현재의 도서와 멀티 미디어 매체의 접목은 수동적 접근으로 이루어지고 있으나 게임이 진행되는 동안 정확한 위치로 빠르게 이동하여 시각적, 청각적 효과를 제공함에는 한계를 가지고 있다. 이러한 단점을 해소하기 위해 스토리 북 게임용도서의 콘텐츠 상에 펜으로 갖다 대면 콘텐츠의 내용을 판별하고 펜에서 외부기기인 IPTV로 판별된 내용을 전달하고 TV로 콘텐츠의 내용을 출력할 수 있게끔 콘텐츠 패턴 인식 및 블루투스를 지원하는 지능형 펜 설계를 제안한다. 이는 기존의 도서에서 제공되지 않는 출판물에 대한 직접 인덱싱 기능을 부가함으로 빠른 접근으로 게임 참여자인 유아들의 집중력 향상 및 이야기 전개의 재미를 높이는 효과를 보인다.

The objective of this study is to evaluate the mechanical behaviors and structural integrity of the weldment of high strength steel by using an acoustic emission (AE) techniques. Simple tension and AE tests were conducted against the 3 kind of welding test specimens. In order to analysis the effectiveness of weldability, joinability and structural integrity, we used K-means clustering method as a unsupervised learning pattern recognition algorithm for obtained multivariate AE main data sets, such as AE counts, energy, amplitude, hits, risetime, duration, counts to peak and rms signals. Through the experimental results, the effectiveness of the proposed method is discussed.

To understand the pattern recognition from dataset, a study should be started from the decomposition process of context into a collection of data pieces because the context may infer different words or information. Many researchers have been focused on finding an effective methodology for data storage, retrieval, representation, and discovery. As a similar endeavor, this paper proposes a new modeling method using group theory and situation theory. This paper provides how to construct a semi-group as a modeling method for pattern recognition from huge dataset. This process of construction of semi‐groups can be used as a retrieval tool for the decomposed information if necessary.

This paper proposes a pattern recognition and classification algorithm based on a circular structure that can reflect the characteristics of the sEMG (surface electromyogram) signal measured in the arm without putting the placement limitation of electrodes. In order to recognize the same pattern at all times despite the electrode locations, the data acquisition of the circular structure is proposed so that all sEMG channels can be connected to one another. For the performance verification of the sEMG pattern recognition and classification using the developed algorithm, several experiments are conducted. First, although there are no differences in the sEMG signals themselves, the similar patterns are much better identified in the case of the circular structure algorithm than that of conventional linear ones. Second, a comparative analysis is shown with the supervised learning schemes such as MLP, CNN, and LSTM. In the results, the classification recognition accuracy of the circular structure is above 98% in all postures. It is much higher than the results obtained when the linear structure is used. The recognition difference between the circular and linear structures was the biggest with about 4% when the MLP network was used.

This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform ‘tying up shoe’ using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user’s intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

Surface electromyogram (sEMG), which is a bio-electrical signal originated from action potentials of nerves and muscle fibers activated by motor neurons, has been widely used for recognizing motion intention of robotic prosthesis for amputees because it enables a device to be operated intuitively by users without any artificial and additional work. In this paper, we propose a training-free unsupervised sEMG pattern recognition algorithm. It is useful for the gesture recognition for the amputees from whom we cannot achieve motion labels for the previous supervised pattern recognition algorithms. Using the proposed algorithm, we can classify the sEMG signals for gesture recognition and the calculated threshold probability value can be used as a sensitivity parameter for pattern registration. The proposed algorithm was verified by a case study of a patient with partial-hand amputation.

In this paper, damage assessment technology based on statistical pattern recognition technology was developed for maintenance of structure and the performance of the developed technology was verified by vibration test. The damage assessment technique uses the improved Mahalanobis distance theory, which is a statistical pattern recognition technique, and developed to take account of the variability between the measured data. In order to verify the damage evaluation performance of the developed technology, a cable damage test was conducted for a cable-stayed bridge. Experimental results show that the developed damage assessment technology has the capability of extracting information that can determine the location of damage due to cable damage.