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Framework Design of an Edge Gateway System Supporting Multi-Protocol Standardized Access Detection 支持多协议标准化接入检测的边缘网关系统框架设计
IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-05-20 DOI: 10.20965/jaciii.2023.p0431
Xiaoyan Zhao, Rui Chen, Jianwei Li, Chunlei Li, Yan Chen, Tian-yao Zhang, Z. Zhang
Recently, intelligent city construction has been promoted with the development of the Internet of things (IoT). The edge IoT gateway plays a critical role as the data aggregation core and processing center. Most existing gateways mainly solve heavy data storage and processing loads in cloud computing centers. There is less attention paid to multi-protocol data transmission and fusion. However, multiple products with different protocols in an IoT system require a flexible gateway compatible with multiple protocols. This paper proposes a multi-protocol edge gateway. The frame design was based on the actual demand for edge data acquisition. The gateway hardware platform used an RK3399 chip transplanted from the embedded operating system. It could support simultaneous multi-protocol access to ZigBee, LoRa, Bluetooth, and Wi-Fi. We combined the plug-and-play (PnP) hardware device access detection scheme with the system onboard interface driver to realize dynamic access detection and unified device management. In addition, the gateway also integrated data storage and access functions and partial edge computing functions. Finally, the experiment results verified that the multi-protocol edge gateway could meet the demand for data access and device control.
近年来,随着物联网(IoT)的发展,智慧城市建设得到了推动。边缘物联网网关作为数据汇聚核心和处理中心,发挥着至关重要的作用。现有的网关主要解决云计算中心的数据存储和处理负荷。对多协议数据传输和融合的研究较少。然而,在物联网系统中,不同协议的多种产品需要灵活兼容多种协议的网关。本文提出了一种多协议边缘网关。框架的设计是基于边缘数据采集的实际需求。网关硬件平台采用嵌入式操作系统移植的RK3399芯片。它可以同时支持对ZigBee、LoRa、蓝牙和Wi-Fi的多协议访问。我们将即插即用(PnP)硬件设备访问检测方案与系统板载接口驱动程序相结合,实现了动态访问检测和统一设备管理。此外,网关还集成了数据存储访问功能和部分边缘计算功能。最后,实验结果验证了多协议边缘网关能够满足数据访问和设备控制的需求。
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引用次数: 0
Motion Analysis and Experiment of Multiple Magnetic Small-Scale Soft Robots 多磁性小型软机器人运动分析与实验
IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-05-20 DOI: 10.20965/jaciii.2023.p0340
Pan Zhang, Wenjie Qin, Haoyun Ma, Jundong Wu, Yangwu Wang
Since magnetic field is penetrating and harmless to human body, magnetic soft robots driven by magnetic field have great potential in medical fields. Thus, magnetic soft robots have attracted wide attention. However, the current researches mainly focus on the design of a single magnetic soft robot. Multiple magnetic soft robots also deserve to be studied due to their applications in collaborative operation. This paper presents a new design and fabrication method of multiple magnetic small-scale soft robots with different magnetic strength, size, and length-width ratio. The robots can be controlled to move in different motion modes and motion states under identical magnetic field. By analyzing their magnetic response property, which is the switching conditions between the two motion modes, and analyzing their states of the walking motion, two robots are selected from a batch of fabricated robots to carry out experiment. The results show that the two robots can move in different motion modes in the identical magnetic field.
由于磁场具有穿透性,对人体无害,因此磁场驱动的磁性软机器人在医学领域具有很大的应用潜力。因此,磁性软机器人引起了广泛的关注。然而,目前的研究主要集中在单磁软机器人的设计上。多磁体软机器人在协同作业中的应用也值得研究。提出了一种具有不同磁性强度、尺寸和长宽比的多磁体小型软机器人的设计与制造新方法。在相同的磁场下,可以控制机器人在不同的运动模式和运动状态下运动。通过分析它们的磁响应特性,即两种运动模式之间切换的条件,并分析它们的行走运动状态,从一批已制成的机器人中选择两个机器人进行实验。结果表明,在相同的磁场下,两个机器人可以以不同的运动方式运动。
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引用次数: 0
Study of Changes over Time in Breath Alcohol Concentration and Brain Function Induced by Low-Impact Drinking 低冲击饮酒引起的呼吸酒精浓度和脑功能随时间变化的研究
IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-05-20 DOI: 10.20965/jaciii.2023.p0438
Yuichi Sato, Kosuke Nagano, Fumiya Kinoshita, Hideaki Touyama
Currently, Japan’s Road Traffic Act uses the breath alcohol concentration (BrAC), which is the concentration of alcohol in 1 L of breath, to enforce the law. However, 400 drunk driving accidents occurred in 2020 when the BrAC was below 0.15 mg/L, which is legal limit in Japan, and cognitive function may be impaired at this concentration. Therefore, we investigated the effects of alcohol consumption at levels below a BrAC of 0.15 mg/L on cognitive function and its change over time using P300, a type of event-related potential, and the concomitant negative variation (CNV). Beer with a 5% alcohol content was used as the drinking load, and the amount of alcohol consumed was determined using a derivation formula that varied according to the subject’s body weight. Electroencephalogram (EEG) measurements were obtained five times before drinking and 10, 30, 50, and 70 min after, and participants were given a go/no-go task to induce event-related potentials during the measurements. The BrAC was measured immediately before the EEG measurement was performed. The results revealed that the alcohol concentration in the exhaled breath increased significantly (p<0.05) at 10, 30, and 50 min after drinking compared with immediately before. In addition, the P300 latency was significantly prolonged (p<0.05), and the early and late CNV amplitudes were significantly increased (p<0.05) in the event-related potentials during these time periods. Moreover, no significant difference was observed in expiratory alcohol concentration at 70 min post-drinking, but significant changes in event-related potentials still occurred. Therefore, our results suggest that cognitive function is impaired in patients with low levels of alcohol consumption, when the BrAC is below the legal limit, and even when the BrAC recovers after the end of alcohol consumption.
目前,日本的《道路交通法》使用呼气酒精浓度(BrAC)来执法,即每升呼气中的酒精浓度。然而,在2020年,当BrAC低于日本法定限值0.15 mg/L时,发生了400起酒驾事故,认知功能可能在这个浓度下受损。因此,我们利用P300(一种事件相关电位)和伴随的负变异(CNV)研究了BrAC低于0.15 mg/L水平时饮酒对认知功能的影响及其随时间的变化。酒精含量为5%的啤酒作为饮酒负荷,酒精消耗量根据受试者体重的不同使用推导公式确定。在饮酒前、饮酒后10分钟、30分钟、50分钟和70分钟进行5次脑电图(EEG)测量,并在测量期间给参与者一个go/no-go任务来诱发事件相关电位。在脑电图测量之前立即测量BrAC。结果显示,与饮酒前相比,饮酒后10、30、50 min呼出气体中酒精浓度显著升高(p<0.05)。P300潜伏期显著延长(p<0.05),事件相关电位的早、晚CNV振幅显著升高(p<0.05)。此外,饮酒后70分钟呼气酒精浓度无显著差异,但事件相关电位仍发生显著变化。因此,我们的研究结果表明,低水平饮酒的患者,当BrAC低于法定限制时,甚至当BrAC在饮酒结束后恢复时,认知功能都会受损。
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引用次数: 0
Dynamics Simulation of Biped Robot with Arch Structure and Toe Joint 具有足弓结构和足趾关节的两足机器人动力学仿真
IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-05-20 DOI: 10.20965/jaciii.2023.p0404
Reona Nekomoto, A. Sekiguchi
The arch structure of human foot absorbs impact and assists push-off movements during walking. The objective of this study is to introduce arch structures and toe joints into a biped robot, verify the effects, and devise walk control methods by dynamics simulation. We simulated the upright state and start of walking using Choreonoid. The results confirmed that the arch structure improved the impact absorption and stability in the anteroposterior direction. In addition, the arch structure could be expected to smooth the load transfer between the supporting legs during the step change.
人的足弓结构吸收冲击,并协助推动运动在走路。本研究的目的是将足弓结构和足趾关节引入双足机器人,并通过动力学仿真验证其效果,设计步行控制方法。我们使用Choreonoid模拟直立状态和步行开始。结果证实,弓形结构改善了前后方向的冲击吸收和稳定性。此外,在台阶变化过程中,拱结构可以平滑支撑腿之间的荷载传递。
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引用次数: 0
Particle Swarm Optimization-Based Convolutional Neural Network for Handwritten Chinese Character Recognition 基于粒子群优化的卷积神经网络手写体汉字识别
IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-03-20 DOI: 10.20965/jaciii.2023.p0165
Yongping Dan, Zhuo Li
Recently, handwritten Chinese character recognition has become an important research field in computer vision. With the development of deep learning, convolutional neural networks (CNNs) have demonstrated excellent performance in computer vision. However, CNNs are typically designed manually, which requires extensive experience and may lead to redundant computations. To solve these problems, in this study, the particle swarm optimization approach is incorporated into the design of a CNN for handwritten Chinese character recognition, reducing redundant computations in the network. In this approach, each network architecture is represented by a particle, and the optimal network architecture is determined by continuously updating the particles until a global particle is identified. The experimental validation resulted in a network accuracy of 97.24% with only 1.43 million network parameters. Therefore, it is demonstrated that the proposed particle swarm optimization method can quickly and accurately find the optimal network architecture.
近年来,手写体汉字识别已成为计算机视觉的一个重要研究领域。随着深度学习的发展,卷积神经网络(cnn)在计算机视觉方面表现出了优异的性能。然而,cnn通常是手工设计的,这需要丰富的经验,并且可能导致冗余计算。为了解决这些问题,本研究将粒子群优化方法引入到手写汉字识别CNN的设计中,减少了网络中的冗余计算。在该方法中,每个网络结构由一个粒子表示,并通过不断更新粒子来确定最优网络结构,直到识别出全局粒子。实验验证结果表明,仅使用143万个网络参数,网络准确率达到97.24%。实验结果表明,所提出的粒子群优化方法能够快速准确地找到最优网络结构。
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引用次数: 2
Design of Fast Green Distribution Route Based on Greedy Algorithm 基于贪心算法的绿色快速配送路线设计
IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-03-20 DOI: 10.20965/jaciii.2023.p0143
Xiuwu Nie, Kaihui Zhang
This study focused on the fast vehicle route optimization issue with carbon emission and time window constraints for on-time consumer demand based on the greedy approach. A greedy algorithm was established to rapidly plan the distribution route to obtain the lowest distribution cost and shortest distribution time to achieve a green distribution. The distribution cost model covering the costs of vehicle transportation time, time window deviation, fossil consumption, and PM2.5 emission was established based on on-time demand and green distribution characteristics. This study analyzed the milk distribution route for Guangxi A Diary Co., Ltd. based on the greedy algorithm. The results show that compared with the genetic algorithm, the algorithm running time is reduced by 2 s, although the greedy algorithm requires an additional cost of 21.73 CNY.
研究了基于贪心方法的考虑消费者准时需求的碳排放和时间窗约束的快速车辆路线优化问题。建立了一种贪婪算法,快速规划配送路线,以获得最低配送成本和最短配送时间,实现绿色配送。基于准时需求和绿色配送特征,建立了涵盖车辆运输时间、时间窗偏差、化石燃料消耗和PM2.5排放成本的配送成本模型。本研究基于贪心算法对广西A乳业有限公司的牛奶配送路线进行了分析。结果表明,与遗传算法相比,贪心算法的运行时间缩短了2 s,但需要增加21.73 CNY的开销。
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引用次数: 0
Modeling and Control Strategies for Liquid Crystal Elastomer-Based Soft Robot Actuator 基于液晶弹性体的柔性机器人执行器建模与控制策略
IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-03-20 DOI: 10.20965/jaciii.2023.p0235
Jundong Wu, Yawu Wang, Wenjun Ye, Jinhua She, C. Su
Liquid crystal elastomer is a type of soft material with unique physical and chemical properties that offer a variety of possibilities in the growing field of soft robot actuators. This type of material is able to exhibit large, revertible deformation under various external stimuli, including heat, electric or magnetic fields, light, etc., which may lead to a wide range of different applications such as bio-sensors, artificial muscles, optical devices, solar cell plants, etc. With these possibilities, it is important to establish modeling and control strategies for liquid crystal elastomer-based actuators, to obtain the accurate prediction and description of its physical dynamics. However, so far, existing studies on this type of the actuators mainly focus on material properties and fabrication, the state of art on the modeling and control of such actuators is still preliminary. To gain a better understanding on current studies of the topic from the control perspective, this review provides a brief collection on recent studies on the modeling and control of the liquid crystal elastomer-based soft robot actuator. The review will introduce the deformation mechanism of the actuator, as well as basic concepts. Existing studies on the modeling and control for the liquid crystal elastomer-based actuator will be organized and introduced to provide an overview in this field as well as future insights.
液晶弹性体是一种具有独特物理和化学性质的软材料,在机器人软作动器领域的发展提供了多种可能性。这种类型的材料能够在各种外部刺激下表现出大的,可逆的变形,包括热,电场或磁场,光等,这可能会导致广泛的不同应用,如生物传感器,人造肌肉,光学设备,太阳能电池工厂等。基于这些可能性,建立基于液晶弹性体的作动器的建模和控制策略,以获得其物理动力学的准确预测和描述是非常重要的。然而,目前对该类致动器的研究主要集中在材料性能和制造方面,对该类致动器的建模和控制的研究还处于初级阶段。为了更好地从控制的角度了解该主题的研究现状,本文简要介绍了基于液晶弹性体的软机器人执行器的建模和控制方面的最新研究进展。本文将介绍致动器的变形机理,以及基本概念。本文将对基于液晶弹性体的致动器的建模和控制的现有研究进行组织和介绍,以提供该领域的概述以及未来的见解。
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引用次数: 1
Research on Texture Classification Based on Multi-Scale Information Fusion 基于多尺度信息融合的纹理分类研究
IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-03-20 DOI: 10.20965/jaciii.2023.p0207
Lin Wang, Lihong Li, Yaya Su
Texture feature is an important visual cue for an image, which is the unified description of human visual attributes and sensory attributes. The inherent problem of texture image is that the difference of intra-class images is large and the disparity of inter-class images is small. This problem increases the difficulty of texture image recognition. Therefore, improving the relevance embedding of intra-class images can reduce the classification errors caused by this problem. To solve this problem, this paper proposes a multi-scale information fusion network algorithm, which adopts a cascade structure. It combines multi-scale feature information with the corresponding background information. The shallow background information guides the next stage of feature formation and enhances the similarity of intra-class images. The intra-class feature information obtained is more general. The algorithm has been tested on data sets describable texture database (DTD) and Flickr material dataset (FMD), which has achieved good results.
纹理特征是图像重要的视觉线索,是人类视觉属性和感官属性的统一描述。纹理图像固有的问题是类内图像差异大,类间图像差异小。这个问题增加了纹理图像识别的难度。因此,改进类内图像的相关嵌入可以减少这一问题带来的分类误差。为了解决这一问题,本文提出了一种采用级联结构的多尺度信息融合网络算法。它将多尺度特征信息与相应的背景信息相结合。浅层背景信息指导下一阶段的特征形成,增强类内图像的相似性。获得的类内特征信息更加通用。该算法在可描述纹理数据库(DTD)和Flickr材料数据集(FMD)上进行了测试,取得了良好的效果。
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引用次数: 0
The Impact of Field-Flipped Courses on College Students' Self-Regulated Learning and Learning Performance Take a National University in Central Taiwan as an Example 田野翻转课程对大学生自主学习和学习绩效的影响——以台湾中部某国立大学为例
IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-03-20 DOI: 10.20965/jaciii.2023.p0281
Yu-Ling Chen, Shihmin Lo, Jen-Son Cheng
Objectives: In the past, an inherent dilemma in the education field was the difficulty in stimulating self-regulated learning. Flipped education, i.e., flipped teaching and learning, changed the teaching model, with a strategy of increasing students’ active learning during class time through a transformation of teaching and learning methods that enable students to build learning and knowledge on their own. This study investigates the impact of field-based flipped courses on college students taking up self-regulated learning and their learning performance. Methods: This study considers a national university in central Taiwan that adopts 34 field flipped teaching courses and 796 non-degree students from four colleges across all grades as the research objects, and conducts statistical analysis using descriptive statistics, t-test, ANOVA, Pearson’s correlation, and regression analysis on questionnaires to evaluate the association among variables. Findings: 1. With respect to the understanding of the uniqueness of field-based flipped teaching before and after the courses, there were differences among students in the Colleges of Education, Humanities, and Management. 2. In terms of students’ learning performance in the course pertaining to mastery over core literacy, there were differences between students of the Colleges of Education and Humanities at the beginning of the flipped-learning course. 3. There were differences among the students of Colleges of Education, Humanities, and Science and Technology in the later stage of the flipped-learning course. 4. Differences were found in the pre-test of learning performance at the grade level. 5. Self-regulated learning correlated with learning performance. 6. Field-based flipped teaching correlated with learning performance. 7. Self-regulated learning had a mediating effect on field-based flipped teaching and learning performance. Innovations: There is a little systematic discussion on the emergence and impact of flipped teaching in higher education currently in Taiwan. The authors found correlations among flipped teaching, self-regulated learning, and learning performance from the data, as well as discovered that self-regulated learning had a mediating effect on learning performance in field-based flipped teaching. Value: Making the university, when the curriculum arrangement and the development of the unique curriculum map of higher education in the future, possible to be linked with the local revitalization thinking in addition to the general curriculum, as well as being closely integrated with the local people and matters through field-flipped courses, and sustainably interacting therewith to practice university social responsibility.
过去,教育领域的一个固有困境是难以激发自主学习。翻转教育,即翻转式的教与学,改变了教学模式,通过教学方法和学习方法的转变,提高学生在课堂上的主动学习,使学生能够自主构建学习和知识。本研究旨在探讨实地翻转课程对大学生自主学习及其学习绩效的影响。方法:本研究以台湾中部某国立大学34门田野翻转教学课程、4个学院各年级796名非学位学生为研究对象,采用描述性统计、t检验、方差分析、Pearson相关及回归分析等方法,对问卷进行统计分析,以评估变量间的关联。发现:1。在课程前后对实地翻转教学独特性的理解上,教育学院、人文学院和管理学院的学生存在差异。在翻转学习课程开始时,教育学院与人文学院的学生在核心素养掌握课程的学习表现上存在差异。3.教育学院、人文学院和理工学院的学生在翻转学习课程的后期阶段存在差异。4. 在年级水平的学习成绩前测中存在差异。5. 自我调节学习与学习绩效相关。6. 实地翻转教学与学习表现相关。7. 自我调节学习对现场翻转教学和学习绩效有中介作用。创新:目前台湾高等教育对于翻转教学的产生与影响,并没有系统性的讨论。作者从数据中发现了翻转教学、自我调节学习和学习绩效之间的相关性,并发现自我调节学习对基于现场的翻转教学的学习绩效具有中介作用。价值:使大学在未来的课程安排和独特的高等教育课程地图的开发中,除了普通课程之外,还可以与当地的振兴思维相联系,并通过实地翻转课程与当地的人和事紧密结合,并与之持续互动,践行大学的社会责任。
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引用次数: 0
A Data-Driven Prediction Model of Blast Furnace Gas Generation Based on Spectrum Decomposition 基于谱分解的高炉煤气生成数据驱动预测模型
IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-03-20 DOI: 10.20965/jaciii.2023.p0304
Lili Feng, Jun Peng, Zhaojun Huang
Blast furnace gas (BFG) is an important secondary energy in the iron and steel industries, and its efficient and reasonable utilization is the key to improving the economic efficiency of enterprises and the level of energy conservation and emission reduction. Aiming at the problems of difficult accurate modeling on the generation process and difficult prediction of real-time flow, this paper proposes a generation prediction model based on spectrum decomposition. Firstly, the major chemical reactions, production process, and data characteristics of blast furnace are analyzed, and the input variables for the prediction model are reasonably selected based on the correlation analysis results. Then, according to the spectrum characteristics, the BFG data is decomposed into low-frequency and medium-frequency parts by two finite impulse response filters. Next, for the low- and middle-frequency components of data, a low-frequency component prediction model based on the support vector regression, and a middle-frequency component prediction model based on the Elman neural network (ENN) are designed respectively. Finally, we decompose the spectrum of the actual industrial production data and find that the spectrum of the decomposed data basically meets the expected target, which verifies the effectiveness of the finite impulse response filters. In addition, we compare the prediction effect of the designed combined model with other models, such as the support vector regression, the back-propagation neural network, and the ENN. The final experimental results show the correctness, effectiveness, and superiority of the combined model and the spectral decomposition method proposed in this paper.
高炉煤气是钢铁工业重要的二次能源,其高效合理利用是提高企业经济效益和节能减排水平的关键。针对发电过程难以准确建模和实时流量难以预测的问题,提出了一种基于频谱分解的发电预测模型。首先,对高炉主要化学反应、生产工艺、数据特征进行分析,并根据相关分析结果合理选择预测模型的输入变量。然后,根据频谱特征,通过两个有限脉冲响应滤波器将BFG数据分解为低频和中频部分。其次,针对数据的低频和中频分量,分别设计了基于支持向量回归的低频分量预测模型和基于Elman神经网络(ENN)的中频分量预测模型。最后对实际工业生产数据的频谱进行分解,发现分解后的数据频谱基本满足预期目标,验证了有限脉冲响应滤波器的有效性。此外,我们还将所设计的组合模型与其他模型(如支持向量回归、反向传播神经网络和ENN)的预测效果进行了比较。最后的实验结果证明了本文所提出的组合模型与光谱分解方法的正确性、有效性和优越性。
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引用次数: 2
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Journal of Advanced Computational Intelligence and Intelligent Informatics
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