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Energy and comfort performance of occupant-centric air conditioning strategy in office buildings with personal comfort devices

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Building Simulation 2022年第5期15卷 899-911页

作者： jingsi zhang Xiang Zhou Song Lei Maohui Luo School of Mechanical Engineering Tongji UniversityShanghai 200092China East China Architectural Design and Research Institute 151 Hankou RoadShanghaiChina

The personal comfort system(PCS)aims to meet individual thermal comfort demands efficiently to achieve higher thermal comfort satisfaction while reducing air conditioning energy consumption.To date,many PCS devices have been developed and evaluated from the perspective of thermal comfort.It will be useful for future PCS development if an approach to quantify the thermal comfort and energy performance of certain PCS devices and their combinations with consideration of user behaviors can be established.This study attempted to fill this gap by integrating thermal comfort experiments,occupancy simulations,usage behavior modeling,and building energy simulation technologies.First,human subject experiments were conducted to quantify the thermal comfort effects of the PCS.Then,the Markov chain model and conditional probability model were employed to describe the room occupancy and PCS usage behaviors.Finally,the extended comfort temperature range and user behavior models were imported into the building energy simulation tool to analyze the energy-saving potential of the PCS.The results show that the use of PCS can significantly improve occupants,thermal comfort and satisfaction rate under both warm and cool conditions.Using a cooling cushion and desktop fan can lift the upper limit of the comfortable temperature to 29.5℃while the heated cushion can extend the lower limit to 15℃.By increasing the air conditioning temperature setpoint by 2℃in summer and reducing by 2.5℃the heating temperature setpoint in winter,PCS devices can reduce heating and air conditioning energy consumption by 25%-40%while maintaining occupants’thermal comfort.

关键词： thermal comfort personal comfort system occupant behavior building energy simulation building energy saving

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Development of data-driven thermal sensation prediction model using quality-controlled databases

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Building Simulation 2022年第12期15卷 2111-2125页

作者： Xiang Zhou Ling Xu jingsi zhang Lie Ma Mingzheng zhang Maohui Luo School of Mechanical Engineering Tongji UniversityShanghai200092China Guangdong Midea Air-Conditioning Equipment Co. LTDGuangdong 528311China

Predicting the thermal sensations of building occupants is challenging,but useful for indoor environment conditioning.In this study,a data-driven thermal sensation prediction model was developed using three quality-controlled thermal comfort databases.Different machine-learning algorithms were compared in terms of prediction accuracy and rationality.The model was further improved by adding categorical inputs,and building submodels and general models for different contexts.A comprehensive data-driven thermal sensation prediction model was established.The results indicate that the multilayer perceptron(MLP)algorithm achieves higher prediction accuracy and more rational results than the other four algorithms in this specific case.Labeling AC and NV scenarios,climate zones,and cooling and heating seasons can improve model performance.Establishing submodels for specific scenarios can result in better thermal sensation vote(TSV)predictions than using general models with or without labels.With 11 submodels corresponding to 11 scenarios,and three general models without labels,the final TSV prediction model achieved higher prediction accuracy,with 64.7%–90.7%fewer prediction errors(reducing SSE by 3.2–4.9)than the predicted mean vote(PMV).Possible applications of the new model are discussed.The findings of this study can help in development of simple,accurate,and rational thermal sensation prediction tools.

关键词： thermal comfort machine learning adaptive thermal comfort multilayer perceptron

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Atomic tracking of thermally-driven structural evolution in 2D crystals:Case of NbSe_(2)

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InfoMat 2024年第2期6卷 90-98页

作者： Baofei Hou Teng zhang Tingting Wang Hongyan Ji Huixia Yang Liangguang Jia Xu Han jingsi Qiao Yu zhang Liwei Liu Hong-Jun Gao Yeliang Wang School of Integrated Circuits and Electronics&Yangtze Delta Region Academy Beijing Institute of TechnologyBeijingthe People's Republic of China Institute of Physics Chinese Academy of SciencesBeijingthe People's Republic of China

Advanced atomic tracking techniques play a critical role in characterizing structural evolution,elucidating fundamental mechanisms of exotic phenomena and tailoring delicate properties.Thermally driven structural modulation in 2D crystals,such as the charge density wave(CDW),often leads to intriguing quantum properties,making them a valuable platform for exploring fundamental physics and potential device applications.However,despite their significance,experimental studies addressing atomic tracking of thermally-driven structural evolution in 2D crystals have been limited.Herein,we utilize high-accuracy variable-temperature atomic tracking measurements with scanning tunneling microscopy(STM)to directly observe a series of structural transitions in a model 2D crystal,namely NbSe_(2).With the atomic tracking technique,we confirm the existence of the universal thermally-driven CDW transition hysteresis between the heating and cooling cycles.This transition hysteresis,characterized by a constant temperature offset,represents a new phenomenon of structural evolution.Our findings provide a feasible method to track CDW transitions at the atomic scale in 2D crystals,significantly contributing to a better understanding and the potential modulation of these materials'functions in nanodevices.

关键词： 2D crystal atomic tracking technique structural evolution temperature hysteresis

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Room zonal location and activity intensity recognition model for residential occupant using passive-infrared sensors and machine learning

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Building Simulation 2022年第6期15卷 1133-1144页

作者： jingsi zhang Ting Zhao Xiang Zhou Jilong Wang Xinyue zhang Chaokui Qin Maohui Luo School of Mechanical Engineering Tongji UniversityShanghai200092China

Given the importance of recognizing indoor occupant’s location and activity intensity to the control of intelligent homes,exploring approaches that can detect occupants’location and activity types while protecting occupants’privacy is helpful.In this study,occupants’zonal location and activity intensity recognition models were developed using passive infrared(PIR)sensors and machine learning algorithms.A PIR sensor array with 15 nodes was employed to monitor indoor occupant’s and cat’s behavior in a case residential building for 71 days.The output signals of PIR sensors varied with different locations and activity intensities.By analyzing the PIR data feature,models were established using six machine learning algorithms and two sets of data.After comparing model performance,the support vector machine(SVM)algorithm was selected to establish the final models.The model input was optimized by accumulating the PIR data.Taking PIR original counting values in 1-minute and 30-minute accumulated data as input features,the optimized SVM model can achieve 99.7%accuracy under the 10-fold cross-validation for the training data set,and 90.9%accuracy for the test data set.The cat’s activity intensity is much weaker than that of occupant,yielding much smaller PIR output,which helped the SVM model to distinguish cat’s activity from occupant’s with>90%accuracy.The model’s recognition accuracy decreases with the decrease of sensor numbers and nine sensors were necessary.The findings obtained in this study support the promising future of applying PIR sensors in smart homes.

关键词： PIR sensor machine learning occupant behavior pet behavior smart home

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Clinical Knowledge-Based Hybrid Swin Transformer for Brain Tumor Segmentation

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Computers, Materials & Continua 2023年第9期76卷 3797-3811页

作者： Xiaoliang Lei Xiaosheng Yu Hao Wu Chengdong Wu jingsi zhang College of Information Science and Engineering Northeastern UniversityShenyang110819China Faculty of Robot Science and Engineer Northeastern UniversityShenyang110819China Faculty of Engineering Macquarie UniversitySydneyNSW2109Australia

Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging(MRI)imaging is crucial in the pre-surgical planning of brain tumor malignancy.MRI images’heterogeneous intensity and fuzzy boundaries make brain tumor segmentation challenging.Furthermore,recent studies have yet to fully employ MRI sequences’considerable and supplementary information,which offers critical a priori knowledge.This paper proposes a clinical knowledge-based hybrid Swin Transformermultimodal brain tumor segmentation algorithmbased on how experts identify malignancies from MRI images.During the encoder phase,a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a Convolutional Neural Network(CNN)-based backbone to represent local features have been constructed.Instead of directly connecting all the MRI sequences,the proposed method re-organizes them and splits them into two groups based on MRI principles and characteristics:T1 and T1ce,T2 and Flair.These aggregated images are received by the dual-stem Swin Transformer-based encoder branch,and the multimodal sequence-interacted cross-attention module(MScAM)captures the interactive information between two sets of linked modalities in each stage.In the CNN-based encoder branch,a triple down-sampling module(TDsM)has been proposed to balance the performance while downsampling.In the final stage of the encoder,the feature maps acquired from two branches are concatenated as input to the decoder,which is constrained by MScAM outputs.The proposed method has been evaluated on datasets from the MICCAI BraTS2021 Challenge.The results of the experiments demonstrate that the method algorithm can precisely segment brain tumors,especially the portions within tumors.

关键词： Brain tumor segmentation swin transformer multimodal clinical knowledge

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Effect of ultrasonic modification on the protective activity of Flammulina velutipes polysaccharide to prevent ethanol-induced injury on GES-1 cells

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Food Science and Human Wellness 2023年第6期12卷 2451-2459页

作者： Jinrong Xiao Xin Chen jingsi zhang Yifan Wang Lei Zhong Qiping Zhan Qiuhui Hu Liyan Zhao College of Food Science and Technology Nanjing Agricultural UniversityNanjing 210095China College of Food Science and Engineering Nanjing University of Finance and EconomicsNanjing 210023China

Flammulina velutipes(F.velutipes)polysaccharides were modified by ultrasound at the rated power of 150 W and 900 W.The monosaccharide composition,ultraviolet-visible,and Fourier transform infrared spectral characteristics of F.velutipes polysaccharides(FVP)and their ultrasonic modification products(U-FVPs)were determined.The protective effects of FVP and U-FVPs on human gastric mucosal cells GES-1 were confi rmed for the first time.The mole ratios of glucose and galactose were decreased and the mole ratio of mannose was increased after ultrasonic modification.Compared with the original FVP and the FVP modifi ed by ultrasound of 150 W(U-FVP1),the FVP modifi ed by ultrasound of 900 W(U-FVP2)could better prevent ethanol-induced damage to GES-1 cells.With increasing ultrasound intensity,the protective effect of FVPs on GES-1 cells was significantly enhanced by more effective prevention of intracellular reactive oxygen species(ROS)production and more promotion of expression of triglyceride factor 2(TFF2),prostaglandin E2(PGE2),epidermal growth factor(EGF),and transforming growth factorβ1(TGF-β1)mRNA.The ultrasonic modifi cation might be an effective way to develop novel F.velutipes polysaccharides that could effectively resist the gastric injury caused by excessive alcohol consumption.

关键词： Flammulina velutipes polysaccharide Ultrasonic modification GES-1 cell Gastric ulcer Ethanol-induced injury

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基于PIR的办公建筑室内人员定位及动作识别模型

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建筑科学 2024年第4期40卷 8-15页

作者：张静思赵婷周翔美的集团中央研究院上海201702 同济大学机械与能源工程学院上海201804

准确识别人行为,是办公场景智能化的基础。本研究通过收集场景内的被动红外(PIR)传感器阵列信号,分析人员在不同位置、不同动作强度组合下的数据特征。首先,基于多种机器学习算法建立人员位置和动作同时识别的模型,相较于KNN、RF、SVM、... 详细信息

准确识别人行为,是办公场景智能化的基础。本研究通过收集场景内的被动红外(PIR)传感器阵列信号,分析人员在不同位置、不同动作强度组合下的数据特征。首先,基于多种机器学习算法建立人员位置和动作同时识别的模型,相较于KNN、RF、SVM、MLP单一模型,Stacking融合模型性能更优、稳定性更好。然后,分析了面向连续数据流时,多时间步长Stacking模型组合表现更优;注重整体指标时,建议选用短时间步长组合模型;注重识别精度时,建议使用长时间步长组合模型。模型识别准确率达0.99,人员位置识别准确率为0.87,人员动作强度识别准确率为0.89。最后,讨论了区域划分和仪器布置对模型的影响。采用大尺度划分方式,人员位置识别准确率提高;小尺度划分方式下,人员定位更精确。可以通过调整PIR传感器的空间布置,减少传感器台数至8台。

关键词：建筑人行为 PIR传感器动作识别机器学习人员位置

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Greenness identification based on HSV decision tree

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Information Processing in Agriculture 2015年第3期2卷 149-160页

作者： Wenzhu Yang Sile Wang Xiaolan Zhao jingsi zhang Jiaqi Feng School of Computer Science and Technology Hebei UniversityBaoding 071002China

Greenness identification from crop images captured outdoors is the important step for crop growth monitoring.The commonly used methods for greenness identification are based on visible spectral-index,such as the excess green index,the excess green minus excess red index,the vegetative index,the color index of vegetation extraction,the combined index.All these visible spectral-index based methods are working on the assumption that plants display a clear high degree of greenness,and soil is the only background element.In fact,the brightness and contrast of an image coming from outdoor environments are seriously affected by the weather conditions and the capture time.The color of the plant varies from dark green to bright green.The back ground elements may contain crop straw,straw ash besides soil.These environmental factors always make the visible spectral-index based methods unable to work correctly.In this paper,an HSV decision tree based method for greenness identification from maize seedling images captured outdoors is proposed.Firstly,the image was converted from RGB color space to HSV color space to avoid influence of illumination.Secondly,most of the background pixels were removed according to their hue values compared with the ones of green plants.Thirdly,the pixels of wheat straws whose hue values were intersected with tender green leaves were eliminated subject to their hues,saturations and values.At last,thresholding was employed to get the green plants.The results indicate that the proposed method can recognize greenness pixels correctly from the crop images captured outdoors.

关键词： Greenness identification Field crop image HSV decision tree Variable illumination Complex background

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基于空调使用行为记录的用户热偏好预测模型及控制方法分析

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电子技术（上海） 2024年第3期53卷 164-167页

作者：张静思美的集团中央研究院上海201702

阐述预测空调用户的个体热偏好有助于智能空调控制技术的发展。分析空调中的嵌入传感器数据以及用户与这些设备的交互行为,采用机器学习算法对空调的设定温度变化进行预测。结果表明,不同用户的空调使用行为在设定温度偏好和调节时间上... 详细信息

阐述预测空调用户的个体热偏好有助于智能空调控制技术的发展。分析空调中的嵌入传感器数据以及用户与这些设备的交互行为,采用机器学习算法对空调的设定温度变化进行预测。结果表明,不同用户的空调使用行为在设定温度偏好和调节时间上存在较大差异。超过60%的用户倾向于将空调温度设置在25~28℃范围内。机器学习模型预测升高设定温度和降低设定温度的准确率为72.1%~87.3%,通过添加月和小时作为输入特征,模型的性能随着样本的增大而提高。

关键词：数据驱动模型智能家居行为分析空调控制

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An Agent-based Simulation Model of Wheat Market Operation:The Benefit of Support Price

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Journal of Systems Science and Systems Engineering 2022年第4期31卷 437-456页

作者： jingsi Huang Fan zhang Jie Song Wei Li Department of Economy and Management North China Electric Power UniversityBaoding071000China Department of Industrial Engineering and Management Peking UniversityBeijing100871China

Grain security is one of the most important issues worldwide.Many developing countries,including China,have adopted the Agriculture Support Price(ASP)program to stimulate farmers’enthusiasm for growing grain,to ensure self-sufficiency in grain and the stable development of the grain market.To propose decision support for the government in designing a more reasonable support price in the ASP program,we formulate an agent-based model to simulate the operation of the wheat market in the harvest period.To formulate the formation process of the market price influenced by farmers’expected sale price,processors’expected purchase price,and the ASP,the time series and regression methods are adopted.Based on the proposed market price model,to quantitatively analyze the grain transaction process and the ASP program’s impacts on market agents,we develop an agent-based simulation model to describe the adaptive evolution and interaction among market agents.Furthermore,we validate and implement the simulation model with public wheat market data.Finally,insights and suggestions about the decision of the ASP program are provided.

关键词： Agent-based simulation(ABS) wheat market agriculture support price decision support public policy

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