Recently, machine learning is widely used to solve optimization problems in various engineering fields. In this study, machine learning is applied to development of a control algorithm for a smart control device for reduction of seismic responses. For this purpose, Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm. A single degree of freedom (SDOF) structure with a smart tuned mass damper (TMD) was used as an example structure. A smart TMD system was composed of MR (magnetorheological) damper instead of passive damper. Reward design of reinforcement learning mainly affects the control performance of the smart TMD. Various hyperparameters were investigated to optimize the control performance of DQN-based control algorithm. Usually, decrease of the time step for numerical simulation is desirable to increase the accuracy of simulation results. However, the numerical simulation results presented that decrease of the time step for reward calculation might decrease the control performance of DQN-based control algorithm. Therefore, a proper time step for reward calculation should be selected in a DQN training process.

The power of legacy media originated from material foundations, not contents. Legacy media has exercised its power through the control of means of publishing, namely rotary machines. Article 21 (3) of the Constitution reflects this. It states that “The standards of news service and broadcast facilities and matters necessary to ensure the functions of newspapers shall be determined by Act.” In the past, newspapers controlled production of information, publication of article, and distribution of newspaper. However, as big technology corporations virtually monopolized the news publishing and distribution process, the nature of legacy media has changed to be in charge of only production of information. The Constitutional Court of the Republic of Korea concluded, “Media diversity is an indispensable premise for democratic society based on pluralism.” However, as big technology corporations and algorithms intervened in news distribution, the market for diversity of opinions market has collapsed. The monopoly of the algorithm’s distribution of articles is unconstitutional. In order to realize diversity of public opinion in a new media environment, regulation must target an algorithm not a rotary press.

기술의 발전으로 스마트 선박과 관련된 다양한 연구가 진행되고 있으며, 기관실을 무인으로 순찰할 수 있는 기관실 순찰 로봇 도 이러한 연구 중의 하나이다. 순찰로봇은 인공지능을 통해 학습된 정보를 기반으로 기관실을 이동하며 기기 정상 유무 및 누수, 누유, 화재 등의 이상 유무를 파악한다. 기관실 순찰로봇에 관한 연구는 인공지능을 이용한 객체 검출에 관한 연구가 주로 진행되고 있으나, 순 찰로봇의 이동 및 제어에 관한 연구는 부족한 상황이다. 이는 순찰로봇이 객체를 검출하더라도 검출한 객체까지 이동할 방법이 없다는 문제를 야기한다. 이에 본 논문에서는 기관실 이상상황 발생 시 빠르게 이상 유무를 파악할 수 있는 기동성을 확보하기 위해, A* 알고리 즘을 적용하여 순찰로봇이 최단경로를 탐색할 수 있는지를 확인하였다. 라이다를 장착한 소형차를 이용하여 선박 기관실을 주행하며 데 이터를 얻어, SLAM으로 매핑하여 지도를 만들었다. 매핑한 지도에서 순찰로봇의 출발 지점과 목표 지점을 설정하고, A* 알고리즘을 적용 하여 출발 지점부터 목표 지점까지 최단 경로를 탐색하는지를 확인하였다. 시뮬레이션 결과 매핑된 지도에서 출발 지점부터 목표 지점까 지의 장애물을 회피하며 최단 경로를 잘 탐색함을 확인 할 수 있었으며, 기관실 순찰로봇에 적용하면 선박안전에 도움이 될 것으로 사료 된다.

The PoN (Proof of Nonce) distributed consensus algorithm basically uses a non-competitive consensus method that can guarantee an equal opportunity for all nodes to participate in the block generation process, and this method was expected to resolve the first trilemma of the blockchain, called the decentralization problem. However, the decentralization performance of the PoN distributed consensus algorithm can be greatly affected by the network transaction transmission delay characteristics of the nodes composing the block chain system. In particular, in the consensus process, differences in network node performance may significantly affect the composition of the congress and committee on a first-come, first-served basis. Therefore, in this paper, we presented a problem by analyzing the decentralization performance of the PoN distributed consensus algorithm, and suggested a fairness control algorithm using a learning-based probabilistic acceptance rule to improve it. In addition, we verified the superiority of the proposed algorithm by conducting a numerical experiment, while considering the block chain systems composed of various heterogeneous characteristic systems with different network transmission delay.

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry’s yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO2 and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of ‘Seolhyang’ (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.