Efficiently Disassemble-and-Pack for Mechanism

In this paper, we present a disassemble-and-pack approach for a mechanism to seek a box which contains total mechanical parts with high space utilization. Its key feature is that mechanism contains not only geometric shapes but also internal motion structures which can be calculated to adjust geometric shapes of the mechanical parts. Our system consists of two steps: disassemble mechanical object into a group set and pack them within a box efficiently. The first step is to create a hierarchy of possible group set of parts which is generated by disconnecting the selected joints and adjust motion structures of parts in groups. The aim of this step is seeking total minimum volume of each group. The second step is to exploit the hierarchy based on breadth-first-search to obtain a group set. Every group in the set is inserted into specified box from maximum volume to minimum based on our packing strategy. Until an approximated result with satisfied efficiency is accepted, our approach finish exploiting the hierarchy.Comment: 2 pages, 2 figure

Emotion Recognition by Video: A review

Video emotion recognition is an important branch of affective computing, and its solutions can be applied in different fields such as human-computer interaction (HCI) and intelligent medical treatment. Although the number of papers published in the field of emotion recognition is increasing, there are few comprehensive literature reviews covering related research on video emotion recognition. Therefore, this paper selects articles published from 2015 to 2023 to systematize the existing trends in video emotion recognition in related studies. In this paper, we first talk about two typical emotion models, then we talk about databases that are frequently utilized for video emotion recognition, including unimodal databases and multimodal databases. Next, we look at and classify the specific structure and performance of modern unimodal and multimodal video emotion recognition methods, talk about the benefits and drawbacks of each, and then we compare them in detail in the tables. Further, we sum up the primary difficulties right now looked by video emotion recognition undertakings and point out probably the most encouraging future headings, such as establishing an open benchmark database and better multimodal fusion strategys. The essential objective of this paper is to assist scholarly and modern scientists with keeping up to date with the most recent advances and new improvements in this speedy, high-influence field of video emotion recognition

M-A3C: A Mean-Asynchronous Advantage Actor-Critic Reinforcement Learning Method for Real-Time Gait Planning of Biped Robot

Bipedal walking is a challenging task for humanoid robots. In this study, we develop a lightweight reinforcement learning method for real-time gait planning of the biped robot. We regard bipedal walking as a process in which the robot constantly interacts with the environment, judges the quality of control action through the walking state, and then adjusts the control strategy. A mean-asynchronous advantage actor-critic (M-A3C) reinforcement learning algorithm is proposed to obtain the continuous state space and action space, and directly obtain the final gait of the robot without introducing the reference gait. We use multiple sub-agents of M-A3C algorithm to train multiple virtual robots independently at the same time in the physical simulation platform. Then we transfer the trained model to the walking control of the actual robot to reduce the number of training on the actual robot, improve the training speed, and ensure the acquisition of the final gait. Finally, a biped robot is designed and fabricated to verify the effectiveness of the proposed method. Various experiments show that the proposed method can achieve the biped robot’s continuous and stable gait planning

Increasing Soil Organic Carbon for Higher Wheat Yield and Nitrogen Productivity

Improving soil organic carbon (SOC) has been considered as a “win-win way” for ensuring high crop productivity and mitigating chemical N input. Improving SOC can achieve higher wheat yield and simultaneously improve nitrogen (N) productivity (defined as kg grain produced per kg total N input from both indigenous and applied N). Two treatments were tested for improving SOC level. The manure treatment involved applying manure for 6 successive years, and the EM treatment involved adding peat and vermiculite once, both combined with optimized in-season N management. The performance of these two systems were compared with a traditional farming system (Control, where only straw was returned each season). N fertilizer input under all three treatments was optimized by in-season N management and was increased by 90.1% and 48.1% under EM and Manure treatments, respectively, as compared with Control. The average wheat yield for the EM and Manure treatments was 9.1 and 9.2 Mg ha–1, respectively, across all three years, which was 18.8% and 19.7% higher, respectively, than that of the Control treatment (7.7 Mg ha–1). The average chemical N application rates for the EM and Manure treatments were 139 and 146 kg ha–1, which were 24.9% and 21.1% lower than those of the Control treatment, respectively. The N productivity was 15.1% and 14.9% which was higher under Manure and EM treatments than that of the Control treatment. The high yield and N productivity were attributed to improved aboveground dry matter and N uptake by wheat, with optimal soil N supply of the root zone. The higher stem number and weight seen in individual plants with increasing SOC resulted in larger spikes and grains at harvest. Our results determined that increasing SOC combined with optimal N management achieve low chemical N input and higher grain yield by increasing productive stems and grains per spike for improving wheat individual growth