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본 연구는 생활 편의 향상을 위해 계획되는 스마트시티 기술과 서비스가 인구감소와 지 역소멸에 대응하여 중소도시, 특히 행정구역 내부에 다양한 여건을 가진 도농복합도시에 서 지역여건에 적합하게 계획될 수 있는 방안을 제안하였다. 경기도 15개 지방자치단체가 수립한 스마트도시계획의 서비스 적용계획 내용을 검토한 결과, 교통 등 현재 스마트시티 기술발전이 많이 이루어진 분야를 중심으로 서비스가 계획 되었고, 행정구역 내부의 여건을 고려하여 공간별 계획을 수립한 경우도 적었다. 도시규모 와 적용범위에 따라 다양한 종류의 사업계획을 수립하여 지원한 스마트시티 공모사업계획 에서도 정부의 솔루션을 따르도록 하면서 해당 지역의 여건을 고려한 계획을 추진하기는 어려운 실정이었다. 지역별 여건과 서비스 수혜자를 폭넓게 고려한 스마트시티 기술 적용 계획이 이루어져야 할 것으로 판단되었다. 스마트시티 기술 적용을 위한 사례지역으로는 도농복합도시인 경기도 포천시를 선정하 고, 14개 읍면동에 대한 토지이용･건축물･인구･통행특성 자료를 분석한 뒤 이를 기반으로 읍면동을 유형화하였다. 포천시의 3개 읍과 동은 도시화가 진행되었고, 11개 면은 산지와 농업적 토지이용이 많은 것으로 나타났다. 3개 읍과 동에는 교통과 주거서비스에 관한 계 획이, 11개 면에는 고령화와 인구감소에 대응하고, 환경문제, 행정구역의 효율적 관리와 재난방지를 도모할 수 있는 스마트시티 기술이 적용되어야 할 것으로 제안하였다.

This study proposed that smart city technologies and services planned to improve living convenience can be planned appropriately for urban and rural complex cities with various conditions inside the administrative district. As a result of reviewing the service application plan of the smart city plan established by the local governments, services were planned focusing on areas where current smart city technology has developed a lot, such as transportation, and space-specific plans were rarely established in consideration of conditions inside administrative districts. A smart city technology application plan should be implemented in consideration of regional conditions and service beneficiaries. Pocheon-si, an urban-rural complex city, was selected as a case area for smart city technology application. The data on land use, buildings, population, and traffic characteristics for 14 towns and villages were analyzed and categorized. In the three towns where urbanization has progressed, plans for transportation and housing services are needed. Smart city technology for aging, population decline, environmental problems, efficient management of administrative districts, and disaster prevention should be applied to 11 villages with high mountainous and agricultural land use.

우리 판례에서 인정하고 있는 작위의무는 보증인적 지위, 즉 법률, 계약, 선행행위, 신의칙 그리고 조리에 의하여 인정되고 있다. 대부분의 경우가 법률의 위반에 해당하는 경우 인정되고 법률의 규정이 없다면 신의칙에 의하여 인정한다. 하지만 정형화된 인정근거의 부재로 모호한 판단이 이루어지고 있는 것이 사실이다. 이와 관련하여 우리는 독일 민법상 판례에 의하여 인정되고 있는 ‘거래안전의무(Verkehrspflicht)’와의 비교를 고려할 수 있다. 거래안전의무는 크게 네 가지의 발생근거로 나뉘어 진다. 위험의 창출, 지배영역책임, 임무인수, 교통의 개시 등이다. 거래안전의무는 법률의 규정 등에 의해서가 아니라, 발생근거에 따라 위험을 인식하고 제거할 가능성이 있는 경우에 한하여 발생한다. 물론 거래안전의무는 독일민법 제823조 제1항이 열거하고 있는 절대권적 보호법익이 직접적 침해에 의해서만 불법행위책임이 발생한다고 하는 흠결을 보완하기 위하여 부작위에 의한 침해도 인정되어야 한다는 것에서 인정되기 시작된 것이다. 물론 우리나라와 독일에서의 불법행위법은 서로 다른 경향을 보이고 있다. 일반 조항으로서의 민법 제750조는 확대적용이 우려되고 있고, 반대로 독일에서는 그 확대를 꾀하고 있다고 할 수 있다. 하지만 독일과는 달리 보호범위의 확대를 통하여 피해자 보호를 두텁게 하기 위하여 부작위에 의한 불법행위의 인정이 필요하며, 그를 위하여 구체화와 유형화가 필요한 시기라 하겠다. 거래안전의무의 인정근거로 인정하고 있는 영역은 다양해진 행위태양들로 하여금 그 경계가 모호해 졌다. 따라서 안전지배, 직업군 그리고 공법적인 의무로 나누어서 검토하였다. 무엇보다도 거래안전의무는 법적의무가 아니다. 따라서 거래안전의무자에게 의무를 부여하기 위해서는 도식화된 성립요건으로는 안되고 각 사안에 맞게 구체적으로 타당한 요건이 필요하다. 이러한 요건을 살펴보기 위하여 크게 위험지배와 직업군으로 분류하였다. 거래안전의무는 위험이 인식되지 않는다면 그리고 위험의 방지 또는 제거가 불가능한 것이라고 하는 경우에 역시 인정되지 않는다. 우선 위험지배에 속하는 경우는, 교통이 빈번한 곳, 즉 통행이 잦은 곳, 공적 공간일수록, 오랜 된 곳일수록, 사용자의 연령층이 낮을수록, 그 위험이 클수록 그리고 위험방지 또는 제거에 필요한 비용이 적을수록 거래안전의무가 인정된다. 그리고 직업군에 속하는 경우는, 특히 대중교통의 운전자인 경우에 보다 높은 정도의 주의의무를 부과한다. 왜냐하면 교통에 참가하는 사람이 많을수록, 위험이 클수록, 위험발생비율이 높을수록 거래안전의무가 인정된다. 모든 위험은 사전에 방지되고 제거될 수 없다. 하지만 언제나 발생할 수 있는 위험에 대하여 안전을 구체화하는 것이 필수적이다.

Above all, the commission recognized in korean case is recognized by following examples, such as guaranteed status, laws, contract, preceding action, fiduciary duty and the reason. Most cases are recognized by the regulation of the law but if there is no regulation that would be recognized by fiduciary duty. However, it is true that ambiguous judgment is being made due to the absence of formal recognition grounds. In this regard, we may consider a comparison with verkehrspflicht recognized by the precedent in German Civil Code. Verkehrspflicht is divided into four main causes. Which is the creation of risk, the responsibility of occupied area, the acquisition of duty and the commencement of traffic. In Germany, verkehrspflicht arises only when there is a possibility of recognizing and removing the risk based on the above grounds, not by the regulation of law. Based on the above recognition ground and requirement, the contents of each area were reviewed. First of all, in order to recognize the Verkehrspflicht within the safety control area. There are some important factors to be considered such as public transportation area, transport of people, public or private use, space for children use, use of many people and passibility. In terms of cleaning and spraying duty, important matter is when to be discharged. Since it is not possible for public officials to deal with a lot of space immediately, it's most important requirement in occurrence of verkehrspflicht that the duty is to be completed from morning to evening. At last, in case of occupational group, there are no formalized and specified requirements, such as those belonging to the other ranges above.

Extraction of knowledge from soccer videos has enormous applications like context-based advertisement, content-based video retrieval, match summarization, and highlight extraction. Overlapping soccer actions and uncontrolled video capturing conditions make it challenging to detect action accurately. For overcoming these problems, Convolutional Neural Network and Recurrent Neural Network are used jointly to classify different lengths of soccer actions. Initially, transfer learning from pre-trained VGG network extracts characteristic spatial features. Afterwards, Gated Recurrent Unit deals with temporal dependency and solves the vanishing gradient problem. Finally, softmax layer assigns decimal probabilities to each class. Experimental results demystify the significance of the proposed architecture.

Although deep learning methods of convolutional neural networks (CNNs) and long-/short-term memory (LSTM) are widely used for text categorization, they still have certain shortcomings. CNNs require that the text retain some order, that the pooling lengths be identical, and that collateral analysis is impossible; In case of LSTM, it requires the unidirectional operation and the inputs/outputs are very complex. Against these problems, we thus improved these traditional deep learning methods in the following ways: We created collateral CNNs accepting disorder and variable-length pooling, and we removed the input/output gates when creating bidirectional LSTMs. We have used four benchmark datasets for topic and sentiment classification using the new methods that we propose. The best results were obtained by combining LTSM regional embeddings with data convolution. Our method is better than all previous methods (including deep learning methods) in terms of topic and sentiment classification.

The first attempt in architectural design theory to consider the perceptual and metaphysical dimension separately with a fully modern scientific manner, which exist in contrast to the Renaissance idea, was Claude Perrault, who emphasized the cognitive factors as an important scientific human issue in building design in 1683 in his book "Ordonnance". Even today many elaborated works to reveal the mysterious design processes based on a set of rational approaches have been introduced since the Design Method movement in 1960's. Their pioneering and challenging efforts to rationalize design process have been mostly rely on the cultural issues whether it takes a qualitative or quantitative stance. On the other hand, however, today's computer generated free form architecture seems not to be aware of those lessons learned from the past and, therefore, this study conducted an extensive research through exploring on morphological building forms with cultural issues to fill up the missing and/or lacking characteristics of today trend in building design.

Online social networks empower individuals with limited influence to exert significant control over specific individuals' lives and exploit the anonymity or social disconnect offered by the Internet to engage in harassment. Women are commonly attacked due to the prevalent existence of sexism in our culture. Efforts to detect misogyny have improved, but its subtle and profound nature makes it challenging to diagnose, indicating that statistical methods may not be enough. This research article explores the use of deep learning techniques for the automatic detection of hate speech against women on Twitter. It offers further insights into the practical issues of automating hate speech detection in social media platforms by utilizing the model's capacity to grasp linguistic nuances and context. The results highlight the model's applicability to information science by addressing the expanding need for better retrieval of hazardous content, scalable content moderation, and metadata organization. This work emphasizes content control in the digital ecosystem. The deep learning-based methods discussed improve the retrieval of data connected to hate speech in the context of a digital archive or social media monitoring system, facilitating study in fields including online harassment, policy formation, and social justice campaigning. The findings not only advance the field of natural language processing but also have practical implications for social media platforms, policymakers, and advocacy groups seeking to combat online harassment and foster inclusive digital spaces for women.

The preferred feature selection methods for text classification are filter-based. In a common filter-based feature selection scheme, unique scores are assigned to features; then, these features are sorted according to their scores. The last step is to add the top-N features to the feature set. In this paper, we propose an improved global feature selection scheme wherein its last step is modified to obtain a more representative feature set. The proposed method aims to improve the classification performance of global feature selection methods by creating a feature set representing all classes almost equally. For this purpose, a local feature selection method is used in the proposed method to label features according to their discriminative power on classes; these labels are used while producing the feature sets. Experimental results obtained using the well-known 20 Newsgroups and Reuters-21578 datasets with the k-nearest neighbor algorithm and a support vector machine indicate that the proposed method improves the classification performance in terms of a widely known metric ($F_1$).

This research proposes a new neural network for text categorization which uses alternative representations of documents to numerical vectors. Since the proposed neural network is intended originally only for text categorization, it is called NTC (Neural Text Categorizer) in this research. Numerical vectors representing documents for tasks of text mining have inherently two main problems: huge dimensionality and sparse distribution. Although many various feature selection methods are developed to address the first problem, the reduced dimension remains still large. If the dimension is reduced excessively by a feature selection method, robustness of text categorization is degraded. Even if SVM (Support Vector Machine) is tolerable to huge dimensionality, it is not so to the second problem. The goal of this research is to address the two problems at same time by proposing a new representation of documents and a new neural network using the representation for its input vector.

With the increasing use of the Internet and electronic documents, automatic text categorization becomes imperative. Several machine learning algorithms have been proposed for text categorization. The k-nearest neighbor algorithm (kNN) is known to be one of the best state of the art classifiers when used for text categorization. However, kNN suffers from limitations such as high computation when classifying new instances. Instance selection techniques have emerged as highly competitive methods to improve kNN through data reduction. However previous works have evaluated those approaches only on structured datasets. In addition, their performance has not been examined over the text categorization domain where the dimensionality and size of the dataset is very high. Motivated by these observations, this paper investigates and analyzes the impact of instance selection on kNN-based text categorization in terms of various aspects such as classification accuracy, classification efficiency, and data reduction.

For text categorization task, distinctive text features selection is important due to feature space high dimensionality. It is important to decrease the feature space dimension to decrease processing time and increase accuracy. In the current study, for text categorization task, we introduce a novel statistical feature selection approach. This approach measures the term distribution in all collection documents, the term distribution in a certain category and the term distribution in a certain class relative to other classes. The proposed method results show its superiority over the traditional feature selection methods.

This research proposes a new strategy where documents are encoded into string vectors and modified version of KNN to be adaptable to string vectors for text categorization. Traditionally, when KNN are used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text categorization, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and modify the supervised learning algorithms adaptable to string vectors for text categorization.

Product categorization has been considered as an effective tool in lowering user search costs and enhancing the discoverability of a product/seller from a large selection of products/sellers [1-2]. As such, many online commerce platforms often create or eliminate product categories to improve the matching between two different parties (e.g., sellers and buyers). It is also well-known that product categorization can be one solution to resolve the adverse effects of superstar (or ranking) effects for sellers. By offering product categories, platform service providers can increase the likelihood of matching between sellers and consumers [3] and create a less competitive marketplace by providing sellers with the opportunity to migrate to a less competitive category [4]. For consumers, product categories are expected to lower their search costs, allowing them to find goods or services that meet their needs more easily [5]. While the benefits of product categorization have been welldocumented, however, there is still a paucity of research concerning how supplier (or sellers) can utilize product re-categorizations especially in digital goods markets. Therefore, this study examines how digital good suppliers can leverage platform-driven product re-categorizations for success in mobile app software markets. Apple App store, the world’s largest app store market, created a new category (i.e., Shopping) in November 2015 and asked app developers to switch voluntarily from the existing category to the new one (i.e., Shopping from Lifestyle category). Using a quasi-experiment design, we attempt to examine the impact of category migration on app performance. To do this, we gathered data on mobile applications (henceforth, app) from Apple App Store recorded from April 9th, 2015, to November 4th, 2016. In our dataset, we observed the migration of apps within 4 weeks after the category split, with different apps moving from Lifestyle to Shopping at different times. Aggregated data on weekly base, we divided migrated apps into four groups based on their migration timing, with the first group moving in the same week as the new category introduction and the other three groups moving in consecutive weeks. For preliminary analyses, we compared the performance of apps four weeks before and after migration using T-test. Overall, the performance measures improved after the category split for both apps stayed in Lifestyle and those moved to Shopping, while movers show greater improvement compared to non-movers. Furthermore, we found that earlier movers revealed better performances than later movers. The preliminary findings indicate that product recategorization helps consumers to find appropriate digital goods for their needs, leading to improved performance. Based on our preliminary findings, we expect that re-categorization of digital goods can benefit both sellers and consumers by facilitating better matching between them. We further anticipate that our study will identify the most effective migration strategies for sellers by comparing the performance of apps based on their migration timing. Moreover, we expect our findings to provide salient contributions to the extant literature by shedding light on how sellers can utilize platform-driven product re-categorizations through migration. On the practical front, we expect to provide valuable information to platform owners and digital goods sellers.

This paper aims to investigate the semantic differences between the French near-synonyms type and genre through a corpus-based distributional analysis and explains these differences in terms of categorization strategies. Although both words denote “type” or “kind” and are often considered interchangeable, actual language use reveals distinct collocational and distributional patterns. Using a large-scale French corpus from Sketch Engine, this study analyzes major syntactic environments of type and genre, including type/genre de + N constructions, modifier combinations, and subject/object positions, based on logDice and ΔlogDice measures. The results show that type tends to function as an objective and structural classificatory category, whereas genre reflects a more interpretive and convention-based categorization associated with cultural and discourse contexts. These differences appear consistently across syntactic environments. This study demonstrates that corpus-based distributional analysis provides an effective approach for explaining semantic distinctions between near-synonyms in terms of categorization patterns.

본 연구의 목적은 사회적, 환경적 측면에서 농공단지 조성효과를 유형화하고, 농공단지 조성효과를 실증적으로 검증하는 것이다. 이를 위해 본 연구에서는 요인분석을 통해 농공단지 조성효과를 유형화하고, 공분산분석을 통해 농공단지 조성효과를 실증적으로 검증하였다. 본 연구의 분석결과를 요약하면 다음과 같다. 첫째, 농공단지 조성효과는 긍정적 측면에서 지역연계 및 고용, 인구증가 및 상권활성화, 사회공헌 및 교류가, 그리고 부정적 측면에서 농촌사회 해체, 환경 및 경관훼손, 불균형 및 재정악화로 구분이 된다. 둘째, 농공단지 조성은 지역연계 및 고용, 환경 및 경관훼손, 농촌사회 해체에 유의미한 영향을 미치지만, 인구증가 및 상권활성화, 사회공헌 및 교류, 불균형 및 재정악화에는 유의미한 영향을 미치지 않는다. 따라서 향후 농공단지를 조성할 때는 환경 및 경관훼손과 농촌사회 해체의 부정적 효과를 최소화하기 위한 사전예방조치가 이루어져야 한다. 그리고 현재 농공단지의 긍정적 효과로 작동하지 못하는 인구증가 및 상권활성화와 사회공헌 및 교류가 촉진될 수 있는 있는 방안도 마련되어야 한다.

This study aims to categorize the effects of developing rural industrial parks from social and environmental perspectives and to verify these effects empirically. To this end, this study categorized the effects of developing rural industrial parks through factor analysis using Cheongyang-gun as a case study, and empirically verified these effects through covariance analysis. The analysis results are summarized as follows. First, the effects of developing a rural industrial park include regional linkage and employment, population growth and revitalization of local commerce, corporate social responsibility and relation in the positive aspects, and include the disintegration of rural society, environmental and landscape damage, imbalance and financial deterioration in the negative aspects. Second, the development of rural industrial parks has a significant impact on regional linkage and employment, environmental and landscape damage, and the disintegration of rural communities, but does not have a significant impact on population growth and revitalization of local commerce, corporate social responsibility and relation, imbalance, and financial deterioration. Therefore, when developing rural industrial parks in the future, preventive measures should be taken to minimize the negative effects. In addition, measures should be prepared to promote population growth and revitalization of local commerce, corporate social responsibility and relation, currently not working as positive effects of rural industrial parks.

본 연구에서는 이제까지 출판된 노벨 엔지니어링 관련 문헌 중 최근 10년 동안 발표된 54편의 문헌들을 선 정하여 수업 주제를 유목화하였다. 수업사례들을 분석한 결과, 환경, 안전, 인성, 민주시민, 생태, 인공지능, 리터 러시, 기타의 8가지 주제로 유목화할 수 있었다. 이 8개의 주제는 2015 개정 교육과정과 2022 개정 교육과정 총론에 기술된 교육과정의 중점 사항과 핵심역량에서 제시한 키워드들이 거의 반영되었다. 이는 수업을 진행한 교사가 개정된 교육과정을 노벨 엔지니어링 수업에 충분히 반영한 결과이다. 그러나 몇몇 주제에 관해서는 중요 성에 비하여 수업사례가 매우 적다. 예를 들면, 민주시민과 리터러시와 관련된 노벨 엔지니어링 수업사례는 3개 로 매우 적었다. 향후에는 다양한 수업 주제가 발표되리라 기대한다. 또한 본 연구로 인하여 앞으로 더 다양한 주제의 수업사례가 발표되어 노벨 엔지니어링 수업을 시도하려는 현장 교사들이 자신 있게 융합 수업에 도전할 수 있기를 기대한다.

The study selected 54 papers related to Novel Engineering published in the past 10 years and categorized into class topics. As a result, we can categorize them into eight topics: environment, safety, personality, democratic citizenship, ecology, artificial intelligence, literacy, and others. These eight topics largely reflect the keywords presented in the curriculum's key points and core competencies described in the 2015 Revised Curriculum and the 2022 Revised Curriculum. I think that this results from the effort of teachers who conducted the class sufficiently incorporating the revised curriculum into the Novel Engineering class. However, there are not many class examples for some topics. For example, there were only three class examples related to democratic citizenship and literacy. I look forward to seeing a variety of class topics presented in the future. Furthermore, we hope that this study will lead to the publication of case studies on a wider range of topics, enabling teachers in the field to confidently attempt integrated classes in Novel Engineering.

논문은 일본무용의 범주화와 관련된 용어의 혼란을 정리함과 동시에 국가를 배경으로 하는 전통예술장르 범주의 일반적인 문제점을 살펴보고자 한다. 현재 공연 되는 일본무용을 구체적으로 살펴보면 세 가지 범주로 나눌 수 있다. 첫째, 가부키에 뿌리를 둔 무용이다. 교토의 마이코(舞子)가 연회 석상에서 추는 춤인 자시키마이와 가부키 무용이 대표적이다. 둘째, 전통사회의 상류층이 향유한 춤이다. 노가쿠(能樂) 에 등장하는 춤, 황실 및 신사에서 거행하는 가구라(神樂)에 기반한 춤 등이 대표적 이다. 셋째, 민속춤이다. 각 지방에 따라 행해지는 마쓰리에서 추는 춤이 일례다. 첫 번째 범주의 무용이 가장 널리 공연되기 때문에 일본무용이 곧 가부키 무용으로 인식되는 경우가 많다. 결론적으로, 일본무용 연구에서 용어의 혼란은 범주의 혼란 에 따른 결과다. 국내 일본무용 연구에서 사용되는 니혼부요는 첫 번째 범주의 무용 을 다른 범주의 무용과 구별하기 위해 사용한다고 볼 수 있다. 본 논문은 포괄적 시각으로 일본무용의 면면을 살펴봄으로써 앞서 언급한 ‘일본무용’과 ‘니혼부요’의 혼용 문제를 분류와 범주화 차원에서 정리하였다. 이러한 명칭의 혼용은 상당 부분 ‘니혼부요’의 폐쇄성에 기인하며 이는 전통예술의 계승 과정에서 불가피하게 마주하 는 문제이기도 하다. 본 논문은 이를 루만과 에스포지토를 통해 설명함으로써 범주 의 오류를 넘어 명칭의 혼용 문제가 내포한 근본적인 문제에 주목하고자 하였다

This article begins with a question regarding the confusion surrounding the Korean translation of a Japanese traditional dance. The term "nihonbuyo" is found in academic papers, while "ilbonmuyong" is commonly used to refer to Japanese traditional dance. The Japanese word consists of four Chinese characters: 日本舞踊. Authors who use the term "nihonbuyo" aim to distinguish the Japanese traditional dance that is based on kabuki, or Japanese traditional musicals, from other traditional dances performed throughout Japan. This confusion highlights two important aspects of Japanese traditional dance. Firstly, kabuki plays a pivotal role in Japanese traditional dance. Secondly, it is necessary to distinguish between a narrow definition of Japanese traditional dance, which is based on kabuki dance, and a broader definition that encompasses all types of Japanese traditional dance. This article introduces major subcategories of Japanese traditional dance, which continue to reproduce Japanese traditional dance through everyday practice.

현재 한국은 OECD 국가 중에서 대학병원을 비롯한 상급병원의 접근이 매우 용이하고, 환자가 대학병원의 응급실에 한 두시간 내 당도 할 수 있는 반면, 적절한 시점에 수술을 받지 못해 사망하는 소위 ‘필수의료’ 사각지대가 많이 발생하고 있다. 특히, 외과계 필수의 료(외필의)의 경우 기본적인 수술임에도 현재 대학병원에서조차 응급상황에서 외필의 수술을 집도할 의료진이 부족하는 등 전체적인 의사수는 많지만 외필의 의사수는 매우 부족한 상황이다. 이러한 인력난은 수술에 대한 낮은 보험수가 문제도 있지만, 응급상황 에서 발생률이 높아지는 의료사고에 대한 형사책임의 부담감도 매우 큰 영향을 미친다. 본 글에서는 필수의료에 대한 해결책으로 융합된 스마트 외과계 필수의료(SES) 병원 에서 인력난을 해결하기 위해서 형사면책을 제시하고 있다. 현재 형사면책의 필요성에 대해서는 의료계에서는 공감하지만, 국민적 합의를 이루지 못하고 있는 것은 모호한 면 책범위에 대한 부분이다. 이 글에서는 의료행위 중에서 형사면책이 가능한 필수의료 행 위(수술)에 대한 명확한 범주를 마련하고자 한다.

Korea has very easy access to tertiary hospitals, including university hospitals, among OECD countries, and patients can reach the emergency room of a university hospital within 1-2 hours. However, there are many so-called 'essential medical' blind spots where people die because they do not receive surgery in time. In particular, in the case of essential medical care in the surgical field, despite basic surgery, there is a shortage of medical staff to perform outpatient surgery in emergency situations at university hospitals. Although this lack of manpower has a problem with low insurance premiums for surgery, it also has a very large impact on the burden of criminal liability for medical malpractice, which increases the incidence in case of emergencies. Here, we propose crime immunity to solve the manpower shortage of converged smart surgical essential medical (SES) hospitals. Currently, the medical community agrees on the need for crime immunity, but it is an ambiguous scope of immunity that has not reached a national consensus. We would like to present clear standards for essential medical practices (surgery) that require criminal immunity.