This paper realizes the estimation of classroom occupancy by using the CO2 sensor and deep learning technique named Long-Short-Term Memory. As a case of connection with IoT and machine learning, I achieve the model to estimate the people number in the classroom based on the environmental data exported from the CO2 sensor, I also evaluate the performance of the model to show the feasibility to apply our module to the real environment.

随着智能建筑应用的增长，住宅建筑中的占用信息变得越来越重要。在智能建筑物的范式的背景下，为了广泛的目的，需要这种信息，包括提高能源效率和乘员舒适性。在这项研究中，使用基于电器技术信息的深度学习实施了住宅建筑中的占用检测。为此，提出了一种新型的智能住宅建筑系统占用方法。通过智能计量系统测量的电器，传感器，光和HVAC的数据集用于模拟。为了对数据集进行分类，使用了支持向量机和自动编码器算法。混淆矩阵用于准确性，精度，召回和F1，以证明所提出的方法在占用检测中的比较性能。拟议的算法使用电器的技术信息达到95.7〜98.4％。为了验证占用检测数据，采用主成分分析和T分布的随机邻居嵌入（T-SNE）算法。通过使用占用检测，智能建筑物中可再生能源系统的功耗降低到11.1〜13.1％。

公共收费站占用预测在开发智能充电策略方面发挥了重要意义，以减少电动车辆（EV）操作员和用户不便。然而，现有研究主要基于具有有限的准确度的传统经济学或时间序列方法。我们提出了一种新的混合长期内记忆神经网络，其包括历史充电状态序列和时间相关的特征，用于多步离散充电占用状态预测。与现有的LSTM网络不同，所提出的模型将不同类型的特征分开，并用混合神经网络架构处理它们。该模型与许多最先进的机器学习和深度学习方法进行了比较，基于从英国邓迪市的开放数据门户网站获得的EV充电数据。结果表明，该方法分别产生非常准确的预测（99.99％和81.87％，分别前进（10分钟）和6个步骤（1小时），优于基准接近的（+ 22.4％）前方预测和6步前方的预测和6.2％）。进行灵敏度分析，以评估模型参数对预测精度的影响。

A digital twin is defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision-making. Unfortunately, the term remains vague and says little about its capability. Recently, the concept of capability level has been introduced to address this issue. Based on its capability, the concept states that a digital twin can be categorized on a scale from zero to five, referred to as standalone, descriptive, diagnostic, predictive, prescriptive, and autonomous, respectively. The current work introduces the concept in the context of the built environment. It demonstrates the concept by using a modern house as a use case. The house is equipped with an array of sensors that collect timeseries data regarding the internal state of the house. Together with physics-based and data-driven models, these data are used to develop digital twins at different capability levels demonstrated in virtual reality. The work, in addition to presenting a blueprint for developing digital twins, also provided future research directions to enhance the technology.

这项工作提出了一种用于参与感测的无线传感器网络的提议，其中IOT传感装置特别用于监测和预测空气质量，作为高成本气象站的替代方案。该系统称为PMSening，旨在测量颗粒材料。通过将原型收集的数据与来自车站的数据进行比较来完成验证。比较表明，结果是关闭的，这可以为问题提供低成本解决方案。该系统仍然呈现了使用反复性神经网络的预测分析，在这种情况下，在这种情况下，预测呈现与实际数据相关的高精度。

5G无线技术和社会经济转型的最新进展带来了传感器应用的范式转移。 Wi-Fi信号表明其时间变化与身体运动之间存在很强的相关性，可以利用这些变化来识别人类活动。在本文中，我们证明了基于时间尺度Wi-Fi通道状态信息的自由互助人与人类相互作用识别方法的认知能力。所检查的共同活动是稳定的，接近，离职的，握手的，高五，拥抱，踢（左腿），踢（右腿），指向（左手），指向（右手），拳打（左手），打孔（右手）和推动。我们探索并提出了一个自我发项的双向封盖复发性神经网络模型，以从时间序列数据中对13种人类到人类的相互作用类型进行分类。我们提出的模型可以识别两个主题对相互作用，最大基准精度为94％。这已经扩展了十对对象，该对象对围绕交互 - 转变区域的分类得到了改善，从而确保了88％的基准精度。同样，使用PYQT5 Python模块开发了可执行的图形用户界面（GUI），以实时显示总体相互交流识别过程。最后，我们简要地讨论了有关残障的可能解决方案，这些解决方案导致了研究期间观察到的缩减。这种Wi-Fi渠道扰动模式分析被认为是一种有效，经济和隐私友好的方法，可在相互的人际关系识别中用于室内活动监测，监视系统，智能健康监测系统和独立的辅助生活。

With the evolution of power systems as it is becoming more intelligent and interactive system while increasing in flexibility with a larger penetration of renewable energy sources, demand prediction on a short-term resolution will inevitably become more and more crucial in designing and managing the future grid, especially when it comes to an individual household level. Projecting the demand for electricity for a single energy user, as opposed to the aggregated power consumption of residential load on a wide scale, is difficult because of a considerable number of volatile and uncertain factors. This paper proposes a customized GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM) architecture to address this challenging problem. LSTM and GRU are comparatively newer and among the most well-adopted deep learning approaches. The electricity consumption datasets were obtained from individual household smart meters. The comparison shows that the LSTM model performs better for home-level forecasting than alternative prediction techniques-GRU in this case. To compare the NN-based models with contrast to the conventional statistical technique-based model, ARIMA based model was also developed and benchmarked with LSTM and GRU model outcomes in this study to show the performance of the proposed model on the collected time series data.

我们在澳大利亚墨尔本郊区的K-12私立学校进行了一个田间研究。数据捕获包含两个元素：首先，使用两个室外气象站的5个月纵向场研究，以及17个教室的室内气象站和乘员控制的房间空调的通风口上的温度传感器;这些在5分钟的测井频率下为每个教室的各个数据集中的各个数据集，包括乘员存在的额外数据。数据集用于推出乘员如何运营房间空调单元的预测模型。其次，我们在4周的横断面研究en-gage中跟踪了23名学生和6名教师，使用可穿戴传感器来记录生理数据，以及日常调查来查询乘客的热舒适度，学习参与，情绪和座位行为。总的来说，组合的数据集可用于分析校园内室内/室外气候和学生行为/精神状态之间的关系，这为未来设计智能反馈系统的机会为学生和员工受益。

A well-performing prediction model is vital for a recommendation system suggesting actions for energy-efficient consumer behavior. However, reliable and accurate predictions depend on informative features and a suitable model design to perform well and robustly across different households and appliances. Moreover, customers' unjustifiably high expectations of accurate predictions may discourage them from using the system in the long term. In this paper, we design a three-step forecasting framework to assess predictability, engineering features, and deep learning architectures to forecast 24 hourly load values. First, our predictability analysis provides a tool for expectation management to cushion customers' anticipations. Second, we design several new weather-, time- and appliance-related parameters for the modeling procedure and test their contribution to the model's prediction performance. Third, we examine six deep learning techniques and compare them to tree- and support vector regression benchmarks. We develop a robust and accurate model for the appliance-level load prediction based on four datasets from four different regions (US, UK, Austria, and Canada) with an equal set of appliances. The empirical results show that cyclical encoding of time features and weather indicators alongside a long-short term memory (LSTM) model offer the optimal performance.

技术的改进与时间和时间相关的问题线性相关。已经看到，随着时间的推移，人类面临的问题数量也会增加。然而，解决这些问题的技术也往往会改善。最早的现有问题之一开始于车辆的发明内容是停车位。多年来，使用技术的易于解决这个问题已经发展，但停车问题仍然仍未解决。这背后的主要原因是停车不仅涉及一个问题，而且它包括一系列问题。其中一个问题是分布式停车生态系统中停车槽的占用检测。在分布式系统中，用户将找到优选的停车位，而不是随机停车位。在本文中，我们将基于Web的应用提出了一种用于在不同停车位停车空间检测的解决方案。该解决方案基于计算机视觉（CV），并使用Python 3.0中编写的Django框架构建。解决方案用于解决占用检测问题以及提供用户基于可用性和偏好确定块的选项。我们提出的系统的评估结果是有前途和有效的。所提出的系统也可以与不同的系统集成，并用于解决其他相关停车问题。

电价是影响所有市场参与者决策的关键因素。准确的电价预测非常重要，并且由于各种因素，电价高度挥发性，电价也非常具有挑战性。本文提出了一项综合的长期经常性卷积网络（ILRCN）模型，以预测考虑到市场价格的大多数贡献属性的电力价格。所提出的ILRCN模型将卷积神经网络和长短期记忆（LSTM）算法的功能与所提出的新颖的条件纠错项相结合。组合的ILRCN模型可以识别输入数据内的线性和非线性行为。我们使用鄂尔顿批发市场价格数据以及负载型材，温度和其他因素来说明所提出的模型。使用平均绝对误差和准确性等性能/评估度量来验证所提出的ILRCN电价预测模型的性能。案例研究表明，与支持向量机（SVM）模型，完全连接的神经网络模型，LSTM模型和LRCN模型，所提出的ILRCN模型在电价预测中是准确和有效的电力价格预测。

社交媒体的自杀意图检测是一种不断发展的研究，挑战了巨大的挑战。许多有自杀倾向的人通过社交媒体平台分享他们的思想和意见。作为许多研究的一部分，观察到社交媒体的公开职位包含有价值的标准，以有效地检测有自杀思想的个人。防止自杀的最困难的部分是检测和理解可能导致自杀的复杂风险因素和警告标志。这可以通过自动识别用户行为的突然变化来实现。自然语言处理技术可用于收集社交媒体交互的行为和文本特征，这些功能可以传递给特殊设计的框架，以检测人类交互中的异常，这是自杀意图指标。我们可以使用深度学习和/或基于机器学习的分类方法来实现快速检测自杀式思想。出于这种目的，我们可以采用LSTM和CNN模型的组合来检测来自用户的帖子的这种情绪。为了提高准确性，一些方法可以使用更多数据进行培训，使用注意模型提高现有模型等的效率。本文提出了一种LSTM-Incription-CNN组合模型，用于分析社交媒体提交，以检测任何潜在的自杀意图。在评估期间，所提出的模型的准确性为90.3％，F1分数为92.6％，其大于基线模型。

Occupancy information is useful for efficient energy management in the building sector. The massive high-resolution electrical power consumption data collected by smart meters in the advanced metering infrastructure (AMI) network make it possible to infer buildings' occupancy status in a non-intrusive way. In this paper, we propose a deep leaning model called ABODE-Net which employs a novel Parallel Attention (PA) block for building occupancy detection using smart meter data. The PA block combines the temporal, variable, and channel attention modules in a parallel way to signify important features for occupancy detection. We adopt two smart meter datasets widely used for building occupancy detection in our performance evaluation. A set of state-of-the-art shallow machine learning and deep learning models are included for performance comparison. The results show that ABODE-Net significantly outperforms other models in all experimental cases, which proves its validity as a solution for non-intrusive building occupancy detection.

在本文中，我们研究了使用深层学习技术预测外汇货币对未来波动性的问题。我们逐步展示如何通过对白天波动率的经验模式的指导来构建深度学习网络。数值结果表明，与传统的基线（即自回归和GARCH模型）相比，多尺寸长的短期内存（LSTM）模型与多货币对的输入相比一致地实现了最先进的准确性，即自动增加和加入模型其他深度学习模式。

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning.

Dysgraphia, a handwriting learning disability, has a serious negative impact on children's academic results, daily life and overall wellbeing. Early detection of dysgraphia allows for an early start of a targeted intervention. Several studies have investigated dysgraphia detection by machine learning algorithms using a digital tablet. However, these studies deployed classical machine learning algorithms with manual feature extraction and selection as well as binary classification: either dysgraphia or no dysgraphia. In this work, we investigated fine grading of handwriting capabilities by predicting SEMS score (between 0 and 12) with deep learning. Our approach provide accuracy more than 99% and root mean square error lower than one, with automatic instead of manual feature extraction and selection. Furthermore, we used smart pen called SensoGrip, a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios.

With the increasing enrichment and development of the financial derivatives market, the frequency of transactions is also faster and faster. Due to human limitations, algorithms and automatic trading have recently become the focus of discussion. In this paper, we propose a bidirectional LSTM neural network based on an attention mechanism, which is based on two popular assets, gold and bitcoin. In terms of Feature Engineering, on the one hand, we add traditional technical factors, and at the same time, we combine time series models to develop factors. In the selection of model parameters, we finally chose a two-layer deep learning network. According to AUC measurement, the accuracy of bitcoin and gold is 71.94% and 73.03% respectively. Using the forecast results, we achieved a return of 1089.34% in two years. At the same time, we also compare the attention Bi-LSTM model proposed in this paper with the traditional model, and the results show that our model has the best performance in this data set. Finally, we discuss the significance of the model and the experimental results, as well as the possible improvement direction in the future.

存在几种数据驱动方法，使我们的模型时间序列数据能够包括传统的基于回归的建模方法（即，Arima）。最近，在时间序列分析和预测的背景下介绍和探索了深度学习技术。询问的主要研究问题是在预测时间序列数据中的深度学习技术中的这些变化的性能。本文比较了两个突出的深度学习建模技术。比较了经常性的神经网络（RNN）长的短期记忆（LSTM）和卷积神经网络（CNN）基于基于TCN的时间卷积网络（TCN），并报告了它们的性能和训练时间。根据我们的实验结果，两个建模技术都表现了相当具有基于TCN的模型优于LSTM略微。此外，基于CNN的TCN模型比基于RNN的LSTM模型更快地构建了稳定的模型。

城市地区空气质量的提高是公共政府机构的主要关切之一。这种令人担忧从空气质量与公共卫生之间的证据中出现。政府机构在该领域的主要努力包括监测和预测系统，禁止更多污染物机动车，以及在低质量空气期间的交通限制。在这项工作中，提出了一项关于受监管停车服务的动态价格的提案。停车服务的动态价格必须阻止当预测低质量剧集时停车。为此目的，评估不同的深度学习策略。它们共同使用了集体空气质量测量来预测城市空气质量的标签。该提案是通过在马德里（西班牙）的经济参数和深度学习质量标准的评估。

在免费增值游戏中，玩家的收入来自于应用内购买以及该玩家所曝光的广告。玩家玩游戏越长，他或她将在游戏中产生收入的机会就越高。在这种情况下，能够及时检测玩家即将退出比赛（Churn）以做出反应并尝试将玩家保留在游戏中，从而延长他或她的游戏寿命非常重要。在本文中，我们调查了如何通过使用不同的神经网络体系结构组合顺序和汇总数据来改善流失预测中最新的最新预测。比较分析的结果表明，两种数据类型的组合可以根据纯粹的顺序或纯聚合数据来提高预测准确性比预测因子。