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.

This paper deals with the dynamic control of redundant robot manipulator. Traditionally, the kinematic control schemes for redundant robot manipulator were developed from the point of speed and used under the assumption that the dynamic control of manipulator is perfect. However, in reality, the precise control of redundant robot manipulator is very difficult due to their dynamics. Therefore, the kinematic controllers for redundant robot manipulator were employed in the acceleration dimension and may be combined with the computed torque method to achieve the accurate control performance. But their control performance is limited by the accuracy of the manipulator parameters such as the link mass, length, moment of inertia and varying payload. Hence in this paper, the proportional and derivative control gains of the computed torque controller are optimized by the genetic algorithm on the typical payloads, and the neural network is applied to obtain the proper control gains for arbitrary loads. The simulation results show that the proposed control method has better performance than the conventional control method for redundant robot manipulator.

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.

The model predictive controller performance of the mobile robot is set to an arbitrary value because it is difficult to select an accurate value with respect to the controller parameter. The general model predictive control uses a quadratic cost function to minimize the difference between the reference tracking error and the predicted trajectory error of the actual robot. In this study, we construct a predictive controller by transforming it into a quadratic programming problem considering velocity and acceleration constraints. The control parameters of the predictive controller, which determines the control performance of the mobile robot, are used a simple weighting matrix Q, R without the reference model matrix Ar by applying a quadratic cost function from which the reference tracking error vector is removed. Therefore, we designed the predictive controller 1 and 2 of the mobile robot considering the constraints, and optimized the controller parameters of the predictive controller using a genetic algorithm with excellent optimization capability.

After CNN basic structure was introduced by LeCun in 1989, there has not been a major structure change except for more deep network until recently. The deep network enhances the expression power due to improve the abstraction ability of the network, and can learn complex problems by increasing non linearity. However, the learning of a deep network means that it has vanishing gradient or longer learning time. In this study, we proposes a CNN structure with MLP layer. The proposed CNNs are superior to the general CNN in their classification performance. It is confirmed that classification accuracy is high due to include MLP layer which improves non linearity by experiment. In order to increase the performance without making a deep network, it is confirmed that the performance is improved by increasing the non linearity of the network.

Due to growth of electronics and control devices, automation and situational awareness systems have been applied by automobile. Vision systems with the introduction of unmanned system were being actively developed. In this paper, the distortion in the 7-inch LCD screen for the treatment process are divided into Online and Offline processing. Offline processing based on the image signal processing and for generating LUT Online to Offline generated by processing the distortion is applied to the LUT. LUT is applied to distort the image processing in real time, so that distortion correction is made for the purpose of setting.

본 연구는 차도용 칼라 역청 콘크리트의 적용성을 평가하기 위하여 수행되었다. 칼라 역청 포장은 차도가 아닌 보행자 도로나 자전거 도로에 사용되어 왔다 본 연구에서 바인더 개질 및 혼합물 강성의 증진을 위하여 두 가지폴리머가 사용되었다. 또한 갭입도 혼합물에서 나타나는 침강현상을 방지하기 위하여 폐신문지를 사용하였다 마샬안정도, 간접인장강도, 동결-융해 및 인공노화 처리 전 후의 인장강도비, 소성변형 저항성, 피로수명을 측정하였다. LDPE 개질된 칼라 역청 콘크리트는 일반 아스팔트 혼합물에 비해 높은 안정도와 인장강도를 나타냈으며, 특히 소성변형 저항성에서 많은 향상을 가져왔다. Rosin 개질된 칼라 역청 콘크리트는 일반 아스팔트 혼합물보다 높은 안정도와 동결융해저항성을 보였으며, 특히 간접인장강도에서 많은 향상을 보였다. LDPE +Rosin의 개질은 거의 대부분의 역학적 특성의 증가를 가져왔다. 따라서 본 연구에서 사용된 칼라 역청 콘크리트 혼합물은 기존 AP-3를 사용한 아스팔트 콘크리트 혼합물과 비교해 역학적 특성이 비슷하였으며, 모든 혼합물은 차도용 시방 규정을 만족하였다. 그리고, 이를 LDPE와 Rosin으로 개질할 경우 포장의 공용성능이 더욱 향상될 것으로 보여 차도용으로 충분히 사용 가능함을 확인하였다.

This research was conducted to investigate the influence of various organic substrates on growth and yield of ginseng seedling grown organically in the closed plastic house. The pH and EC of substrates used for organically ginseng cultivation ranged 5.93~6.78 and 0.03~0.15 dS/m respectively. The concentrations NH4-N and NO3-N respectively was 14.01~68.63 mg/L, 5.60~58.83 mg/L. The average quantum of the closed plastic house was range from 10 to 16% of natural light. In July and August, the maximum temperature of the closed plastic house did not exceed 30 and the average temperature was maintained within 25 lower than the field because air conditioning ran. The PPV-1 and PPV-2 bed soil substrates produced higher stem length, stem diameter, shoot fresh weight and leaf area than those of conventional culture. In PPV-2 bed soil substrates, root fresh weight and root diameter was the highest. The root fresh weight of PPV-2 bed soil substrates in closed plastic house was maximum 25% heavier than the conventional cultivation. The results of this experiment will be utilized for making new substrate application for organic ginseng culture in the plastic house.

최근 지구 온난화와 더불어 급격한 기후 변화 등으로 여름철 주간 냉방 수요가 급격히 증가하고 있다. 이에 따른 전력 수요량 또한 증가하고 있다. 이런 이유로 세계적으로 에너지 이용 효율 향상에 대한 관심이 높아지고 있고, 최근 국내 및 국외의 축냉시스템을 이용한 주간 전력 피크값을 최소화 하는 연구개발과 실제 적용 사례가 늘어나고 있다. 빙축열시스템의 경우 그 경제적 효과가 높아 그 관심이 높아지고 있는 추세이다. 축냉시스템은 주간 냉방에 사용하는 냉열을 야간에 만들어 탱크에 저장해 두었다가 그것을 낮에 이용함으로써, 갑작스런 부하 증가에 적절히 대응할 수 있는 등 여러 가지 장점을 갖고 있다. 본 실험에서는 슬러리아이스 생성을 위해 냉각표면에서 생성된 슬러리아이스를 분리시키기 위해 유체보다 밀도가 낮은 역전유동 물질을 삽입하여 역전 유동층을 형성하였으며, 역전유동층의 유동에 의해 관군으로 구성되어 있는 냉각튜브 표면에 빙부착이 심화되기 전에 얼음 입자를 분리시켜 수 내지 수십 미크론 단위의 슬러리아이스를 생성하도록 하였다.