도로 위 노면전차 트램를 포함한 다양한 이동수단 흐름을 원활하게 유지하기 위해서는 효율적인 교통 신호 제어가 필요하다. 검증 되지 않은 기술의 현장 평가는 교통안전 측면에서 위험하기에 대부분 가상환경을 통해 적용 기술검증을 선행한다. 본 연구는 다양한 교통신호 제어 알고리즘을 센터 수준에서 적용하는 가상 실험환경 마련을 위한 기능적 요구사항을 정의한다. 기능적 요구사항으로 가 상환경 센터 기반으로 실험을 시작하거나 중지하는 기능, 교통량 등 입력값을 입력하는 기능 등의 기본적 요구사항을 도출하였다. 이 렇게 정의된 기능적 요구사항은 향후 트램 등 다양한 교통수단을 대상으로 하는 가상환경 센터 구축 과정에 효율적으로 참조될 수 있 을 것으로 기대된다.

This paper aims to study the modeling and controller of an electrically driven tractor optimized for energy efficiency under off-road conditions and when subjected to loads such as plowing. The dynamic model design is aimed at a 30kW electric tractor. The vehicle model consists of a 30kW motor, transmission, wheels, and a controller, designed using the commercial software Matlab/Simulink. In order to optimize energy efficiency under load conditions, this paper designs and implements a PID controller focusing on the vehicle's speed and wheel slip. The newly proposed electric tractor modeling and PID controller aim to demonstrate improved energy efficiency through simulation.

새만금 내에서는 종종 식물플랑크톤이 증식하기에 알맞은 환경조건이 생성되며 일시에 식물플랑크톤 대증식이 발생하면서 조 류 관리기준을 초과하는 사례가 발생하고 있다. 이를 대비하기 위하여 과학적 예측기법을 토대로, 식물플랑크톤의 종별로 가장 효과적이 고 효율적인 녹조발생 억제 방안을 제안하기 위하여 식물플랑크톤 대증식 가능성을 예측하고, 제어할 수 있는 모델을 개발하였다. 즉, 하 천에서 유입하는 영양염(DIN, PO4-P)을 정책적으로 조절하고, 갑문운영을 통해 호 내 염분을 제어하는 것이다. 먼저 관측치로부터 인공신 경망 알고리즘을 이용해 식물플랑크톤 대증식 가능성을 예측 결과, 모델의 Kappa 수는 0.7889 ~ 1.0000의 범위로, good ~ excellent 수준이었 다. 다음으로 Garson 알고리즘을 이용하여 종별로 설명변수의 중요도를 평가하였고, 또한 DIN 및 염분 값의 변화에 따른 식물플랑크톤 대 량 증식 확률을 예측하였다. 그 결과, 각 종별로 식물플랑크톤의 대증식을 억제할 수 있는 DIN과 염분 농도를 정량적으로 예측할 수 있었 다. 따라서, 향후 새만금과 같은 거대한 인공 호수에서 식물플랑크톤의 대증식을 억제하기 위한 효율적이고 효과적인 대응방안을 마련할 수 있도록 녹조제어모델을 활용할 수 있을 것으로 판단된다.

In this study, the load fluctuation of the main engine is considered to be a disturbance for the jacket coolant temperature control system of the low-speed two-stroke main diesel engine on the ships. A nonlinear PID temperature control system with satisfactory disturbance rejection performance was designed by rapidly transmitting the load change value to the controller for following the reference set value. The feed-forwarded load fluctuation is considered the set points of the dual loop control system to be changed. Real-coded genetic algorithms were used as an optimization tool to tune the gains for the nonlinear PID controller. ITAE was used as an evaluation function for optimization. For the evaluation function, the engine jacket coolant outlet temperature was considered. As a result of simulating the proposed cascade nonlinear PID control system, it was confirmed that the disturbance caused by the load fluctuation was eliminated with satisfactory performance and that the changed set value was followed.

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 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.

자동 관개 시스템에서는 관수를 자동으로 개시하고 중지할 수 있는 기준값의 설정이 중요하다. 관수 기준값은 작물의 종류와 생육 시기, 토성, 용적 밀도 등에 따라 달라지는 포장 용수량의 토양 수분값으로 결정되기 때문에, 전문적인 지식과 분석 경험이 필요하여 현장 농업인이 직접 파악하는 것은 어렵다. 그래서 재배 작물의 명칭, 재배 지역 및 재배 토양의 토성 등을 조건 변수로 하여 적절한 토양 수분값을 데이터베이스로부터 추출하고, 작물의 종류 및 생육 시기별 토양수분 기준을 데이터베이스화하여 선택한 작물에 적합한 토양수분 장력값을 설정할 수 있는 알고리즘을 개발하였다. 이 알고리즘을 센서부, 제어부, 구동부로 구성되어 있는 시스템에 적용하여 토양 수분을 제어할 수 있는 시스템을 개발하였다. 실험구별로 수분 제어 기준값을 설정하여 측정한 수분값이 -33 kPa 실험구에서 부합률 97.3%, -25 kPa 실험구에서 부합률 96.6%의 결과를 나타내었다. 이 시스템을 이용하여 최근 농촌지역의 고령화와 노동인구 감소에 따른 생산성 감소를 억제하는데 기여할 것으로 사료된다.

This paper proposes a model predictive controller of robot manipulators using a genetic algorithm to secure the best performance by performing parameter optimization with the genetic algorithm. Genetic algorithm is a natural evolutionary process modeled as a computer algorithm and has excellent performance in global optimization, so it is useful for tuning control parameters. The sliding mode controller and inverse dynamics controller are included in the lower part of the model prediction controller to minimize the problems caused by non-linearity and uncertainty of the robot manipulator. The performance superiority of the proposed method as described above has been confirmed in detail through a simulation study.

A tilted tall building is actively constructed as landmark structures around world to date. Because lateral displacement responses of a tilted tall building occurs even by its self-weight, reduction of seismic responses is very important to ensure structural safety. In this study, a smart tuned mass damper (STMD) was applied to the example tilted tall building and its seismic response control performance was investigated. The STMD was composed of magnetorheological (MR) damper and it was installed on the top floor of the example building. Control performance of the STMD mainly depends on the control algorithn. Fuzzy logic controller (FLC) was selected as a control algorithm for the STMD. Because composing fuzzy rules and tuning membership functions of FLC are difficult task, evolutionary optimization algorithm (EOA) was used to develop the FLC. After numerical simulations, it has been seen that the STMD controlled by the EOA-optimized FLC can effectively reduce seismic responses fo the tilted tall building.

본 연구는 얼음결정체의 형성을 막고자 step-cooling 알고리즘을 적용하여 갈치를 과냉각 저장하였다. 저장의 신선도 유지효과를 확인하기 위해 냉장 및 냉동 저장된 갈치와의 신선도 비교평가를 실시하였다. 과냉각 저장은 냉장 저장과 비교하였을 때, 일반세균수와 단백질 부패로 인해 그 함량이 증가되는 VBN, TMA 값에서 비교적 작은 값을 보여 품질 유지에 효과를 나타내었다. 또한, 냉동 저장과 비교하였을 때, pH, VBN 및 TMA에서는 저장이 종료된 12일을 기준으로 차이를 크게 나타내지 않았다. 일반 세균수에서는 9일차까지 비슷한 값을 유지하였으며, 12일 차에서는 과냉각 시료가 높은 값을 보였다. 이를 통해, 과 냉각 저장이 미생물 생장을 최소화하고 단백질 부패를 지연시키는데 효과가 있다고 사료된다. 장기저장에서는 크게 영향을 미치지는 않았으나, 단기저장 관점에서는 냉장 저장보다 과냉각 저장이 갈치의 품질을 유지하는데 많은 장점을 가질 것으로 판단된다.

A smart tuned mass damper (TMD) is widely studied for seismic response reduction of various structures. Control algorithm is the most important factor for control performance of a smart TMD. This study used a Deep Deterministic Policy Gradient (DDPG) among reinforcement learning techniques to develop a control algorithm for a smart TMD. A magnetorheological (MR) damper was used to make the smart TMD. A single mass model with the smart TMD was employed to make a reinforcement learning environment. Time history analysis simulations of the example structure subject to artificial seismic load were performed in the reinforcement learning process. Critic of policy network and actor of value network for DDPG agent were constructed. The action of DDPG agent was selected as the command voltage sent to the MR damper. Reward for the DDPG action was calculated by using displacement and velocity responses of the main mass. Groundhook control algorithm was used as a comparative control algorithm. After 10,000 episode training of the DDPG agent model with proper hyper-parameters, the semi-active control algorithm for control of seismic responses of the example structure with the smart TMD was developed. The simulation results presented that the developed DDPG model can provide effective control algorithms for smart TMD for reduction of seismic responses.

Control performance of a smart tuned mass damper (TMD) mainly depends on control algorithms. A lot of control strategies have been proposed for semi-active control devices. Recently, machine learning begins to be applied to development of vibration control algorithm. In this study, a reinforcement learning among machine learning techniques was employed to develop a semi-active control algorithm for a smart TMD. The smart TMD was composed of magnetorheological damper in this study. For this purpose, an 11-story building structure with a smart TMD was selected to construct a reinforcement learning environment. A time history analysis of the example structure subject to earthquake excitation was conducted in the reinforcement learning procedure. Deep Q-network (DQN) among various reinforcement learning algorithms was used to make a learning agent. The command voltage sent to the MR damper is determined by the action produced by the DQN. Parametric studies on hyper-parameters of DQN were performed by numerical simulations. After appropriate training iteration of the DQN model with proper hyper-parameters, the DQN model for control of seismic responses of the example structure with smart TMD was developed. The developed DQN model can effectively control smart TMD to reduce seismic responses of the example structure.

In this study, a controlled atmosphere (CA) storage system is proposed as a storage method for prolonging processing period. Persimmon was placed in CA storage at 0.5oC±0.5 for 92 d. The qualities of the stored persimmons were compared to determine the possibility of extending shelf life. ‘Sangjudungsi,’ which was harvested on October 26, 2017, was applied to the persimmons. In order to compare differences according to size, the persimmons were classified into Size No. 2 (170 g) and Size No. 3 (145 g). In the result, the yellowness of CA-stored persimmons was 26.3% lower than that of cold-stored ones, confirming that after-ripening was delayed. The firmness of CAstored and Size No. 3 persimmon was higher than that of cold-stored and Size No. 2 persimmon. Tannin decreased significantly in cold storage, but it tended to increase in CA storage. The sugar content of Size No. 3 was lower than that of Size No. 2, but there was no difference in tendencies according to the storage method. Weight loss in CA storage was lower than that in cold storage. A comparison of color difference, firmness, sugar content, tannin, and weight loss ratio showed that CA storage was more effective in improving shelf life than cold storage.

Robot manipulators are highly nonlinear system with multi-inputs multi-outputs, and various control methods for the robot manipulators have been developed to acquire good trajectory tracking performance and improve the system stability lately. The computed torque controller has nonlinear feedforward control elements and so it is very effective to control robot manipulators. If the control gains of the computed torque controller is adjusted according the payload, then more precise control performance is attained. This paper extends the conventional computed torque controller in the joint space to the Cartesian space, and optimize the control gains for some specified payloads in both joint and Cartesian spaces using genetic algorithms. Also a neural network is employed to have proper control gains for arbitrary payloads using generalization properties of the neural network. Computer simulation results show that the proposed control system for robot manipulators has excellent performance in various conditions.

이 논문에서는 다중 재난을 고려한 복합 구조제어 시스템의 최적 설계방법을 제시한다. 한 가지 유형의 위험에 대해 하나의 시스템이 설계되는 전형적인 구조제어 시스템과는 달리, 구조물의 지진 및 바람에 의한 진동응답을 저감하기 위해 능동 및 수동제어 시스템에 대한 동시 최적 설계방법을 제안하였다. 수치 예로서, 30층 빌딩 구조물에 설치된 30개의 점성 댐퍼와 복합형 질량 감쇠기에 대한 최적 설계문제를 보였다. 최적화 문제를 풀기 위해 자체적응 화음탐색(harmony search, HS)알 고리즘을 채택하였다. 화음탐색 알고리즘은 사람이 연주하는 악기의 튜닝 과정을 모방한 전역 최적화를 위한 메타 휴리스틱 진화 연산방법의 하나이다. 또한 전역 탐색 및 빠른 수렴을 위해 자가적응적이고 동적인 매개변수 조정 알고리즘을 도입하였다. 최적화 설계 결과, 능동 및 수동 시스템이 독립적으로 최적화된 표준적인 복합제어 시스템에 비해 제안한 동시 최적제어 시스템의 성능과 효율성이 우수함을 보였다.

In this study, we divided the process operation scenarios into three categories based on raw water temperature and turbidity. We will select and operate the process operation scenario according to the characteristics of the raw water. The number of algae in the DAF treated water has been analyzed to be less than 100 cells/mL. These results indicated that the DAF process is effective in removing the algae. In addition, the scenario of the integrated management decision algorithm of the DAF process was developed. DAF pilot plants (500 m3/day) process has shown a constantly sound performance for the treatment of raw water, yielding a significantly low level of turbidity (DAF treated water, 0.21~1.56 NTU).