A Robotic Writing Framework-Learning Human Aesthetic Preferences via Human-Machine Interactions

Intelligent robots are required to fully understand human intentions and operations in order to support or collaborate with humans to complete complicated tasks, which is typically implemented by employing human-machine interaction techniques. This paper proposes a new robotic learning framework to perform numeral writing tasks by investigating human-machine interactions with human preferences. In particular, the framework implements a trajectory generative module using a generative adversarial network (GAN)-based method and develops a human preference feedback system to enable the robot to learn human preferences. In addition, a convolutional neural network, acting as a discriminative network, classifies numeral images to support the development of the basic numeral writing ability, and another convolutional neural network, acting as a human preference network, learns a human user’s aesthetic preference by taking the feedback on two written numerical images during the training process. The experimental results show that the written numerals based on the preferences of ten users were different from those of the training data set and that the writing models with the preferences from different users generate numerals in different styles, as evidenced by the Fréchet inception distance (FID) scores. The FID scores of the proposed framework with a preference network were noticeably greater than those of the framework without a preference network. This phenomenon indicates that the human-machine interactions effectively guided the robotic system to learn different writing styles. These results prove that the proposed approach is able to enable the calligraphy robot to successfully write numerals in accordance with the preferences of a human user

A data-driven robotic Chinese calligraphy system using convolutional auto-encoder and differential evolution

The Chinese stroke evaluation and generation systems required in an autonomous calligraphy robot play a crucial role in producing high-quality writing results with good diversity. These systems often suffer from inefficiency and non-optima despite of intensive research effort investment by the robotic community. This paper proposes a new learning system to allow a robot to automatically learn to write Chinese calligraphy effectively. In the proposed system, the writing quality evaluation subsystem assesses written strokes using a convolutional auto-encoder network (CAE), which enables the generation of aesthetic strokes with various writing styles. The trained CAE network effectively excludes poorly written strokes through stroke reconstruction, but guarantees the inheritance of information from well-written ones. With the support of the evaluation subsystem, the writing trajectory model generation subsystem is realized by multivariate normal distributions optimized by differential evolution (DE), a type of heuristic optimization search algorithm. The proposed approach was validated and evaluated using a dataset of nine stroke categories; high-quality written strokes have been resulted with good diversity which shows the robustness and efficacy of the proposed approach and its potential in autonomous action-state space exploration for other real-world applications

DWS-YOLO: A Lightweight Detector for Blood Cell Detection

ABSTRACTPeripheral blood cell detection is an essential component of medical practice and is used to diagnose and treat diseases, as well as to monitor the progress of therapies. Our objective is to construct an efficient deep learning model for peripheral blood cell analysis that achieves an optimized balance between inference speed, computational complexity, and detection accuracy. In this article, we propose the DWS-YOLO blood detector, which is a lightweight blood detector. Our model includes several improved modules, including the lightweight C3 module, the increased combined attention mechanism, the Scylla-IoU loss function, and the improved soft non-maximum suppression. Improved attention, loss function, and suppression enhance detection accuracy, while lightweight C3 module reduces computation time. The experiment results demonstrate that our proposed modules can enhance a detector’s detection performance, and obtain new state-of-the-art (SOTA) results and excellent robustness performance on the BCCD dataset. On the white blood cell detection dataset (Raabin-WBC), the proposed detector’s generalization performance was confirmed to be satisfactory. Our proposed blood detector achieves high detection accuracy while requiring few computational resources and is very suitable for resource-limited but efficient medical device environments, providing a reliable and advanced solution for blood detection that greatly improves the efficiency and effectiveness of peripheral blood cell analysis in clinical practice

Mine Strata Pressure Characteristics and Mechanisms in Gob-Side Entry Retention by Roof Cutting under Medium-Thick Coal Seam and Compound Roof Conditions

Coal is among the most important energy sources, and gob-side entry retention by roof cutting (GERRC) is an innovative non-pillar mining technique that can effectively increase coal recovery rates and avoid coal wastage. To investigate the characteristics of mine strata pressure using the GERRC technique, a field case study under conditions involving a medium-thick coal seam and a compound roof was performed, and the mine strata behavior mechanisms were studied by theoretical analysis. Field monitoring shows that the distributions of the weighting step and strength along the longwall working face are asymmetrical. The periodic weighting length on the entry retaining side is longer than that on the other sides of the longwall working face, and the average increase is appropriately 4 m. Compared to the other sides of the longwall, on the entry retaining side, the periodic weighting strength is weaker, the average pressure is reduced by 2.1 MPa, and the peak pressure is reduced by 10.2 MPa. The lateral distance affected by roof cutting along the longwall is approximately 29.75 m, and the closer to the cutting slit, the more significant the roof cutting effect is. The retained entry becomes stable when it is more than 230 m behind the mining face, and the final cross section of the retained entry can meet the reuse demand of the next mining face. Theoretical analysis shows that the roof pressure mechanism in GERRC can be explained using cantilever beam theory. Within the area affected by roof cutting, the thickness of the immediate roof increases, and the suspension plate length of the roof immediately behind the longwall decreases. Then, the gangue pile in the goaf behind the longwall formed by the immediate roof’s collapse and expansion can support the main roof and other overlying strata much better. Therefore, the rotational breaking angle of the main roof is smaller, the periodic weighting step strength increases, and the periodic weighting decreases. According to the structural state of the surrounding rocks during the entire entry retaining process, the retained entry can be divided into coal support, dynamic pressure and stable entry areas

Error controlled actor-critic

The approximation inaccuracy of the value function in reinforcement learning (RL) algorithms unavoidably leads to an overestimation phenomenon, which has negative effects on the convergence of the algorithms. To limit the negative effects of the approximation error, we propose error controlled actor-critic (ECAC) which ensures the approximation error is limited within the value function. We present an investigation of how approximation inaccuracy can impair the optimization process of actor-critic approaches. In addition, we derive an upper bound for the approximation error of the Q function approximator and discover that the error can be reduced by limiting the KL- divergence between every two consecutive policies during policy training. Experiments on a variety of continuous control tasks demonstrate that the proposed actor-critic approach decreases approximation error and outperforms previous model-free RL algorithms by a significant margin

DWS-YOLO: A Lightweight Detector for Blood Cell Detection

Peripheral blood cell detection is an essential component of medical practice and is used to diagnose and treat diseases, as well as to monitor the progress of therapies. Our objective is to construct an efficient deep learning model for peripheral blood cell analysis that achieves an optimized balance between inference speed, computational complexity, and detection accuracy. In this article, we propose the DWS-YOLO blood detector, which is a lightweight blood detector. Our model includes several improved modules, including the lightweight C3 module, the increased combined attention mechanism, the Scylla-IoU loss function, and the improved soft non-maximum suppression. Improved attention, loss function, and suppression enhance detection accuracy, while lightweight C3 module reduces computation time. The experiment results demonstrate that our proposed modules can enhance a detector’s detection performance, and obtain new state-of-the-art (SOTA) results and excellent robustness performance on the BCCD dataset. On the white blood cell detection dataset (Raabin-WBC), the proposed detector’s generalization performance was confirmed to be satisfactory. Our proposed blood detector achieves high detection accuracy while requiring few computational resources and is very suitable for resource-limited but efficient medical device environments, providing a reliable and advanced solution for blood detection that greatly improves the efficiency and effectiveness of peripheral blood cell analysis in clinical practice.</p

Study on Key Parameter Design and Adaptability Technology of the 110 Mining Method for the Yuwang NO.1 Coal Mine in the Diandong Mining Area

The 110 mining method is a high-efficiency entry-retaining technology without coal pillars or filling materials. At present, there is no precedent for its application in the Huaneng Group. In order to introduce this technology, it is planned to carry out experimental application research in the Yuwang NO.1 Coal Mine of the Huaneng Diandong mining area. The Yuwang NO.1 Coal Mine is a typical coal and gas outburst mine with a coal seam group. In view of the introduction of the 110 mining method under these conditions, first, the geological conditions of the Yuwang NO.1 Coal Mine in the Huaneng Diandong mining area are analyzed, the geological characteristics of the test mining face are summarized, and the practical feasibility of the 110 mining method is analyzed according to the geological characteristics of “one soft, one low, two high, and two complex”. Then, according to the engineering experience, calculations, and analysis, the key parameters of roof cutting of the test mining face in the Yuwang NO.1 Coal Mine are obtained, and with the help of a numerical simulation, the roof-cutting height, the roof-cutting angle, and the blasting parameters are numerically simulated and analyzed. The roof-cutting key parameters of the test face are obtained as follows: the roof-cutting depth is 7 m, the roof-cutting angle is 15°, and the blasting method is continuous hole blasting with 500 mm spacing. After that, according to the coal and gas outburst and the occurrence conditions of coal seams in the Yuwang NO.1 Coal Mine, a variety of gob closure design schemes and gob gas drainage design schemes are put forward, and the field effect investigation scheme is given. After the tunnels and open-off cut of the test coal mine’s first mining face are connected, under the guidance of the above research results, the field blasting test and the 110 mining method entry-retaining test are further carried out to verify the rationality of the design of the roof-cutting parameters and the feasibility of the gangue wall closure and gas drainage design. Furthermore, in the process of the field practice, continuous research is carried out on the stope pressure law and the adjacent layer gas drainage technology under the 110 mining method, and finally, the 110 mining method application technology system in the Diandong Mining Area is formed