Improved Implementation and Evaluation of Wireless Sensor Networks in Intelligent Building

A complete study for the implementation of wireless sensor networks in the intelligent building is presented. We carry out some experiments to find out the factors affecting the network performance. Several vital parameters which are related to the link quality are measured before deploying the actual system. And then, we propose an optimized routing protocol based on the analysis of the test data. We evaluate the deployment strategies to ensure the excellent performance of the wireless sensor networks under the real working conditions. And the evaluation results show that the presented system could satisfy the requirements of the applications in the intelligent building.

Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention.Fortunately,digital watermarking provides an effective method to solve this problem.In order to improve the robustness of the medical image watermarking scheme,in this paper,we propose a novel zero-watermarking algorithm with the integer wavelet transform(IWT),Schur decomposition and image block energy.Specifically,we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,then we decompose the sub-blocks by Schur decomposition.After that,the feature matrix is constructed according to the relationship between the image block energy and the whole image energy.At the same time,we encrypt watermarking with the logistic chaotic position scrambling.Finally,the zero-watermarking is obtained by XOR operation with the encrypted watermarking.Three indexes of peak signal-to-noise ratio,normalization coefficient(NC)and the bit error rate(BER)are used to evaluate the robustness of the algorithm.According to the experimental results,most of the NC values are around 0.9 under various attacks,while the BER values are very close to 0.These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods,which indicates it is more suitable for medical image privacy and security protection.

Fault Diagnosis of an Intelligent Building Facility Using Bayesian Networks

There is great significance to diagnose the fault of an intelligent building facility for fault controlling, repairing, eliminating and preventing. As an example, this paper established a Bayesian networks model f or fault diagnosis of the refrigeration system of an intelligent building facility, gave the networks parameters, and analyzed the reasoning mechanism. Based on the model, some data was analyzed and diagnosed by adopting Bayesian networks reasoning platform GeNIe. The result shows that the diagnosis effect is more comprehensive and reasonable than the other method.

Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review

BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algorithm similar to deep learning,has demonstrated its capability to recognise specific features that can detect pathological lesions.AIM To assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of CNNs and their performance.METHODS The databases PubMed,EMBASE,and the Web of Science and research books were systematically searched using related keywords.Studies analysing pathological anatomy,cellular,and radiological images on HCC or liver masses using CNNs were identified according to the study protocol to detect cancer,differentiating cancer from other lesions,or staging the lesion.The data were extracted as per a predefined extraction.The accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed.The primary outcomes of the study were analysing the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.RESULTS A total of 11 studies that met the selection criteria and were consistent with the aims of the study were identified.The studies demonstrated the ability to differentiate liver masses or differentiate HCC from other lesions(n=6),HCC from cirrhosis or development of new tumours(n=3),and HCC nuclei grading or segmentation(n=2).The CNNs showed satisfactory levels of accuracy.The studies aimed at detecting lesions(n=4),classification(n=5),and segmentation(n=2).Several methods were used to assess the accuracy of CNN models used.CONCLUSION The role of CNNs in analysing images and as tools in early detection of HCC or liver masses has been demonstrated in these studies.While a few limitations have been identified in these studies,overall there was an optimal level of accuracy of the CNNs used in segmentation and classification of liver cancers images.

Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely.However,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model complexity.To alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular diseases.In MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer parameters.We conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 datasets.The results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and MixNets.Specifically,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

Intelligent Medical Diagnostic System for Hepatitis B

The hepatitis B virus is the most deadly virus,which significantly affects the human liver.The termination of the hepatitis B virus is mandatory and can be done by taking precautions as well as a suitable cure in its introductory stage;otherwise,it will become a severe problem and make a human liver suffer from the most dangerous diseases,such as liver cancer.In this paper,two medical diagnostic systems are developed for the diagnosis of this life-threatening virus.The methodologies used to develop thesemodels are fuzzy logic and the neuro-fuzzy technique.The diverse parameters that assist in the evaluation of performance are also determined by using the observed values from the proposed system for both developedmodels.The classification accuracy of a multilayered fuzzy inference system is 94%.The accuracy with which the developed medical diagnostic system by using Adaptive Network based Fuzzy Interference System(ANFIS)classifies the result corresponding to the given input is 95.55%.The comparison of both developed models on the basis of their performance parameters has been made.It is observed that the neuro-fuzzy technique-based diagnostic system has better accuracy in classifying the infected and non-infected patients as compared to the fuzzy diagnostic system.Furthermore,the performance evaluation concluded that the outcome given by the developed medical diagnostic system by using ANFIS is accurate and correct as compared to the developed fuzzy inference system and also can be used in hospitals for the diagnosis of Hepatitis B disease.In other words,the adaptive neuro-fuzzy inference system has more capability to classify the provided inputs adequately than the fuzzy inference system.

Evaluation tool for the thermal performance of retrofitted buildings using an integrated approach of deep learning artificial neural networks and infrared thermography

In most countries,buildings are responsible for significant energy consumption where space heating and air conditioning is responsible for the majority of this energy use.To reduce this massive consumption and decrease carbon emission,thermal insulation of buildings can play an important role.The estimation of energy savings following the improvement of a building’s insulation remains a key area of research in order to calculate the cost savings and the payback period.In this paper,a case study has been presented where deep retrofitting has been introduced to an existing building to bring it closer to a Passivhaus standard with the introduction of insulation and solar photovoltaic panels.The thermal performance of the building with its improved insulation has been evaluated using infrared thermography.Artificial intelligence using deep learning neural networks is implemented to predict the thermal performance of the building and the expected energy savings.The prediction of neural networks is compared with the actual savings calculated using historical weather data.The results of the neural network show high accuracy of predicting the actual energy savings with success rate of about 82%when compared with the calculated values.The results show that this suggested approach can be used to rapidly predict energy savings from retrofitting of buildings with reasonable accuracy,hence providing a practical rapid tool for the building industry and communities to estimate energy savings.A mathematical model has been also developed which has indicated a life-long monitoring will be needed to precisely estimate the benefits of energy savings in retrofitting due to the change in weather conditions and people’s behaviour.

Artificial Intelligence and Network Pharmacology Reveal the Medication Rules of Professor Wang Yu-Ying in the Treatment of Climacteric Syndrome

Objective: The objective of this study was to explore the medication rules and academic ideas of Professor Wang Yu-Ying in the treatment of climacteric syndrome(CLS) and to predict new prescriptions. Materials and Methods: The characteristics of frequency, clustering, four properties, and five flavors were analyzed, and new prescriptions were predicted through an artificial intelligence(AI)-based method. The potential pathways of new prescriptions were explored through network pharmacology-based analysis. Results: The top 16 medicinals used by Professor Wang Yu-Ying in the treatment of CLS included Danggui, Longgu, Muli, Fuling, Chuanxiong, Gancao, Xiangfu, and Tusizi. The AI method was applied to predict the basic prescription for treating CLS: Danggui 15 g, Duanlonggu 30 g, Duanmuli 30 g, Fuling 28 g, Chuanxiong 10 g, Gancao 6 g, Xiangfu 12 g, Tusizi 14 g, etc., Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses showed that the pathogenesis of CLS might be related to the estrogen pathway, involving typical steroid responses. Conclusions: This study summarized Professor Wang's medication experience in the treatment of CLS based on the data mining of clinical diagnoses and treatment cases. The AI method was used to predict the new prescription of CLS treatment, which was found to be reasonable by network pharmacology studies on its multi-target and multi-pathway mechanisms.

Employing Computational Intelligence to Generate More Intelligent and Energy Efficient Living Spaces

Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by the user or other networked devices. With the increasing number of these devices attached to the network, the complexity to configure and control them increases, which may lead to major processing and communication overhead. Hence, the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for, but also offer complex services that emerge from unique combinations of devices. This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming. However, with this ＂smart evolution＂, the cognitive load to configure and control such spaces becomes immense. One way to relieve this load is by employing artificial intelligence （AI） techniques to create an intelligent ＂presence＂ where the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the user＇s needs and behaviours. These AI mechanisms should be embedded in the user＇s environments and should operate in a non-intrusive manner. This paper will show how computational intelligence （CI）, which is an emerging domain of AI, could be employed and embedded in our living spaces to help such environments to be more energy efficient, intelligent, adaptive and convenient to the users.

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

Artificial intelligence plays an essential role in the medical and health industries.Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis.However,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected region.Therefore,an optimized deep convolution network is utilized for analyzing disease-affected regions in this work.Different disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning network.This method identifies affected pixels by the gray wolf hunting process.The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis.The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule.The pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation process.This process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the visionof the Wireless Body Sensor Network (WBSN) to improve the health monitoringsystems and has an enormous impact on the healthcare system forrecognizing the levels of risk/severity factors (premature diagnosis, treatment,and supervision of chronic disease i.e., cancer) via wearable/electronic healthsensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy playsa very significant role in the detection of gastric cancer. Convolutional NeuralNetwork (CNN) has been widely used to diagnose gastric cancer based onvarious feature extraction models, consequently, limiting the identificationand categorization performance in terms of cancerous stages and gradesassociated with each type of gastric cancer. This paper proposed an optimizedAI-based approach to diagnose and assess the risk factor of gastric cancerbased on its type, stage, and grade in the endoscopic images for smarthealthcare applications. The proposed method is categorized into five phasessuch as image pre-processing, Four-Dimensional (4D) image conversion,image segmentation, K-Nearest Neighbour (K-NN) classification, and multigradingand staging of image intensities. Moreover, the performance of theproposed method has experimented on two different datasets consisting ofcolor and black and white endoscopic images. The simulation results verifiedthat the proposed approach is capable of perceiving gastric cancer with 88.09%sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.

Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest energy.Therefore,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become paramount.In this vein,efforts have been made to predict the HL and CL using a univariate approach.However,this approach necessitates two models for learning HL and CL,requiring more computational time.Moreover,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware requirement.In this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D CNN.Considering the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and CL.As the 1D data are not affected by excessive parameters,the pooling layer is not applied in this implementation.Besides,the use of pooling has been questioned by recent studies.The performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

基于5G和人工智能技术的“互联网+”应急救治系统构建与应用

目的:构建基于5G和人工智能技术的全场景“互联网+”应急救治系统,通过互联网和智能化手段将院内服务能力和资源扩展到院外,解决目前应急医疗面临的挑战,如数据采集有难度、网络传输不稳定、协同交互不足以及辅助诊断不够智能等。构建一个以患者为核心的分级智能应急救治体系,实现院内外一体化的远程应急医疗服务。方法:在应急事件发生时,利用“声纹+”身份认证技术确保一线救援人员、急救医生、专家和患者的可信接入,利用5G实现多方远程医疗协作,结合远程实时监护、远程会诊与AI辅助决策,实现应急预案智能生成、病情智能监测预警、医院及路线智能规划,以便远程指导急救并提前制定救治方案。结果:该系统已在清华大学附属北京清华长庚医院、珠海市人民医院、福建省急救中心等多家单位得到应用,成功支持了世界互联网大会、北京冬奥会等多个重大项目的应急保障工作,并在天通苑社区向百万居民提供服务。结论:通过整合智能技术以及网络技术,将应急救治的关口前移,应急救治的能力前移,形成前端急救人员应急施救,后端医院专科医师深度参与的基于5G技术和人工智能技术的智能救治系统,形成远程智能应急救治体系管理规范以及技术标准,开创了一种全地域覆盖、全病种服务、全流程管理的应急救治新模式。

人工智能在外科学教育领域的应用前景

在高等教育中,人工智能和虚拟现实等前沿教育技术被广泛应用于开发虚拟学习资源。因此,人工智能(artificial intelligence,AI)在临床实践中的应用被认为是医学教育中一个很有前景的扩展领域。AI能够基于学习者的表现数据和个性化需求,定制教育路径和提供精准的学习建议。这种个性化的支持不仅增强了教育效果,还可以帮助医师快速地掌握复杂的临床技能和决策能力。AI的4个关键组成部分是机器学习、自然语言处理、人工神经网络和视觉处理,每个部分都在外科学教育中具有潜在的应用前景。在一个医患关系紧张、医学生源相对饱和及手术机会减少的时代,AI还能够分析大量的临床数据,预测患者的康复路径和可能的并发症,为医疗团队提供决策支持。通过优化资源利用和流程管理,AI还有助于降低医疗成本,提供更经济高效的医疗护理服务。文章阐述了目前AI技术的应用及其在促进外科学教育方面的前景。

“情景—结构—要素”视角下复杂灾害治理的情报协同体重构

[目的/意义]灾害复杂化给应急治理提出了新的要求,加强多主体情报协同是必然趋势。针对复杂灾害情景构建不足、情报协同网络结构相对松散固化、情报协同要素缺乏一体化布局的问题,文章提出复杂灾害治理情报协同体的概念,围绕情景、结构、要素进行重构。[方法/过程]首先,在情景认知的基础上采用本体构建复杂灾害情景,并描述了其实例化过程。其次,从情报协同主体的挖掘识别与主体关联的凝聚增强两方面阐述了情报协同网络的结构优化方法。最后,探讨了情报协同要素即数据、决策、流程的强化路径,将情报协同体划分为数据共享体、决策集成体、流程弹性体三个关键构成。[结果/结论]文章为复杂灾害治理的情报协同提供了一个顶层设计框架,有助于政府依托数字化转型提升应急情报能力。

基于短距离跳跃连接的U2-Net+医学图像语义分割

医学图像分割是保障发展智慧医疗系统的先决条件之一。由于原U2-Net+网络的跳跃连接只关注同分辨率所提取的特征,所以在设计时借鉴FR-UNet网络加入中间层,接收深层的上下文信息与浅层提取的高分辨率特征进行整合;并在中间层的下采样使用非对称空洞空间卷积金字塔代替,增加网络模型训练时对边缘信息的关注,并在结构最后加入阈值增强模块,加强对细小特征边缘的识别与分割;同时加入到上采样中,帮助网络更好地提取多尺度特征,增加上下文语义关联。根据正负样本不均衡和难易不同的问题设计了组合的损失函数来监督网络优化。实验结果表明,所提算法在DRIVE、STARE+CHASE_DB1数据集上的F1分数分别提高了1.8%与4.2%,在ISIC2018数据集上的DSC分数提高了2.3%。对分割结果进行可视化后表明,该网络在样本较小的情况下可以充分提取到更加精确的边缘信息和细小的特征信息,提高语义分割的效果,所提算法在医学图像语义分割任务上有更好的表现。

人工智能时代背景下县级综合医院建设的思考与对策探析——以我国西部地区为例

基于我国西部地区县级综合医院“空间改造易、人员引进难”的现实问题,本文总结了人工智能医疗技术的革新与应用对西部地区县级综合医院医护人员工作和患者就医的流程及相应建筑空间功能的改变,提出急诊、门诊和手术室等重点空间优化设计要点和未来县级综合医院建筑空间如何适应人工智能时代发展需要的空间应对策略。

引入用能行为概率和群智能优化的数据驱动高精度小时尺度建筑能耗预测体系

提出了一种改进的用户用能行为概率模型,作为新输入集成入能耗预测中,引入麻雀搜索算法(SSA)用于优化长短期记忆神经网络(LSTM)的超参数选择,建立了高精度小时尺度建筑能耗预测体系。在某建筑中的实际应用显示,相比于传统预测体系,改进的能耗预测体系可以使决定系数R 2平均增大0.201,平均绝对百分比误差(MAPE)平均减小18.10%,均方根误差的变异系数(CV-RMSE)平均减小0.176。

未来医院信息化、物联网应用的探索与实践——以杭州市滨江区妇幼保健院迁建工程为例

以杭州市滨江区妇幼保健院迁建工程项目为例,探索基于信息化及物联网应用下医疗建筑的工程实践。随着智能化医疗的普及,医疗建筑的设计理念和模式也在发生深刻变化。物联网技术的运用,使得医疗建筑不再仅仅满足基本的功能需求,而更多地关注患者的体验以及医疗效率的提升。文章将探讨在信息化普及的背景下,如何打造适应现代医疗需求的智能化设计,并为同类型的工程设计提供参考。

基于U-Net网络的医学图像分割研究综述

近年来随着深度学习技术的快速发展,卷积神经网络(CNN)成为语义分割的重要支撑框架,被广泛运用于多种目标检测与分割的任务当中。在医学图像分割任务中,U-Net网络以其优异的分割性能、可拓展性的网络结构等特点成为该领域研究的热点。如今有众多学者从网络的结构等方面对U-Net进行改进以优化网络性能、提升分割准确度。研究通过对相关文献的分析,首先介绍了基于U-Net的经典改进模型;然后阐述了六大U-Net改进机制:注意力机制、inception模块、残差结构、空洞机制、密集连接结构以及集成网络结构;随后介绍了医学图像分割常用评价指标和非结构化改进方案,这些非结构化改进方法包括数据增强、优化器、激活函数和损失函数四个方面;之后列举并分析了在肺结节、视网膜血管、皮肤病和颅内肿瘤新冠肺炎四大医学图像分割领域的改进模型;最后对U-Net网络的未来发展进行展望,为相关研究提供思路。