Cloud Components Research Articles

Robotic facilities resources of the future mechatronic factories

The practical problem is to project robotic facilities resources, which are used in the future mechatronic factories. Mechatronic factory robotic facilities resources are software and hardware components of dynamic self-organization factories placed in the intelligent robotic systems (IRS). A structural scheme, which describes the IRS components interaction, which is the base for together unified image of robotic facilities resources in the physical IRS (cloud IRS) is shown. A cyclic diagram of the IRS physical and cloud components interaction is shown. A structural scheme, which describes multi-functional convergence of physical IRS and cloud IRS is shown. Obviously, that robotic facilities resources as a cloud service is the most universal form of the future mechatronic factory in the industrial. Robotic facilities infrastructure primary components are defined, which are necessary to project future mechatronic factory, which specialty in engineering.

ВЕБ ОРІЄНТОВАНА СИСТЕМА ДОСТУПУ ДО ВІДДАЛЕНОГО РОБОЧОГО СТОЛУ ТА ПРОГРАМНОГО КОМПЛЕКСУ GRAN У ПРОЦЕСІ НАВЧАННЯ МАТЕМАТИКИ В ШКОЛІ

The article deals with Gran software and the access to it through the remote desktop. The peculiarities of web-based remote desktop access and Gran software system for teaching mathematics at school are revealed. The paper also describes the application of open source software (Ulteo OVD) that allows users to remotely access their desktops and applications using one of the Desktop as a Service cloud components through a client device browser. The modes of using Ulteo OVD client software are described. Two servers (Application Server and Session Manager Server) have been organized to use Ulteo OVD by means of PROXMOX web-based virtual environment. The authors propose the way to use Ulteo OVD to access software installed on a remote server, in particular Gran software. The article also describes some examples of graphical problem solving using Gran1 that can be used in maths at school. The considered issues are quite complex and rather time-consuming without Gran1 software for graphical analysis of various tasks and approximate calculation of numerical measures of different objects (lengths of surfaces, areas of surfaces, volumes of bodies, probabilities of occurrence of different events, statistical analysis of observational data) of their solving. The range of tasks that can be solved with the use of the Gran software package, in particular Gran1 programme, is quite broad. By applying a creative approach, their analysis and solution can have a positive effect on the learners’ mental and cultural development. It is emphasized that the use of various technologies in educational process, as well as the selection of tasks and the content of training in general, should be pedagogically weighted, based on the harmonious combination of pedagogical heritage of the past and modern achievements of scientific and technological progress.

Crowd review and attribute-based credit computation for an access control mechanism in cloud data centers

ABSTRACT The cloud services are very useful and demanded in an information technology industry these days. Cloud services are universally delivered with the help of the internet. Cloud services provide more flexibility than conventional service methods. It is the responsibility of cloud service providers to ensure the provisioning and scaling of resources, reliability, and security of cloud resources. The cloud services witnessed exponential growth in recent years, which created many opportunities for developers but also created many security concerns. To overcome these security and legal issues, there exist various access control mechanisms. In this paper, we review the existing access control mechanism and proposed an access control framework for a cloud computing environment. The conventional access control mechanism does not fulfill the requirement of the cloud environment. The cloud environment has the diverse set of users with different sets of security requirement. It has different security concerns, namely multi-tenant hosting, heterogeneity of users, trust among the cloud components, etc. This paper proposes the innovative approach of crowd review and attribute-based credit computation for the access control mechanism in cloud data centers. In this paper, the concept of attribute-based access control is integrated with a credit component module. The concept of crowdreviewing and reputation is used to compute the credit value of users. The simulation experiment has been shown to protect the consistent users and to restrict the access of inconsistent users in the cloud environment. It is an access control approach to mitigate the challenges in security concerns.

Systemic Autoimmune Disease Among Adults Exposed to the September 11, 2001 Terrorist Attack.

ObjectiveAutoimmune disease is an emerging condition among persons exposed to the September 11, 2001 attack on the World Trade Center (WTC). Components of the dust cloud resulting from the collapse of the WTC have been associated with development of a systemic autoimmune disease, as has posttraumatic stress disorder (PTSD). We undertook this study to determine whether dust exposure and PTSD were associated with an increased risk of systemic autoimmune disease in a 9/11‐exposed cohort.MethodsAmong 43,133 WTC Health Registry enrollees, 2,786 self‐reported having a post‐9/11 systemic autoimmune disease. We obtained informed consent to review medical records to validate systemic autoimmune disease diagnoses for 1,041 enrollees. Diagnoses of systemic autoimmune diseases were confirmed by classification criteria, rheumatologist diagnosis, or having been prescribed systemic autoimmune disease medication. Controls were enrollees who denied having an autoimmune disease diagnosis (n = 37,017). We used multivariable log‐binomial regression to examine the association between multiple 9/11 exposures and risk of post‐9/11 systemic autoimmune disease, stratifying by responders (rescue, recovery, and clean‐up workers) and community members (e.g., residents, area workers).ResultsWe identified 118 persons with systemic autoimmune disease. Rheumatoid arthritis was most frequent (n = 71), followed by Sjӧgren's syndrome (n = 22), systemic lupus erythematosus (n = 20), myositis (n = 9), mixed connective tissue disease (n = 7), and scleroderma (n = 4). Among 9/11 responders, those with intense dust cloud exposure had almost twice the risk of systemic autoimmune disease (adjusted risk ratio 1.86 [95% confidence interval 1.02–3.40]). Community members with PTSD had a nearly 3‐fold increased risk of systemic autoimmune disease.ConclusionIntense dust cloud exposure among responders and PTSD among community members were associated with a statistically significant increased risk of new‐onset systemic autoimmune disease. Clinicians treating 9/11 survivors should be aware of the potential increased risk of systemic autoimmune disease in this population.

New Algorithms for Secure Outsourcing of Large-Scale Systems of Linear Equations

Cloud computing is the on-request accessibility of computer system resources, specially data storage and computing power, without direct dynamic management by the client. In the simplest terms, cloud computing means storing and accessing data and programs over the Internet instead of your computer’s hard drive. Along the improvement of cloud computing, more and more applications are migrated into the cloud. A significant element of distributed computing is pay-more only as costs arise. Distributed computing gives strong computational capacity to the general public at diminished cost that empowers clients with least computational assets to redistribute their huge calculation outstanding burdens to the cloud, and monetarily appreciate the monstrous computational force, transmission capacity, stockpiling, and even reasonable programming that can be partaken in a compensation for each utilization way Tremendous bit of leeway is the essential objective that forestalls the wide scope of registering model for clients when their secret information are expended during the figuring procedure. Critical thinking is a system to arrive at the pragmatic objective of specific instruments that tackles the issues as well as shield from pernicious practices.. In this paper, we examine secure outsourcing for large-scale systems of linear equations, which are the most popular problems in various engineering disciplines. Linear programming is an operation research technique formulates private data by the customer for LP problem as a set of matrices and vectors, to develop a set of efficient privacy-preserving problem transformation techniques, which allow customers to transform original LP problem into some arbitrary one while protecting sensitive input/output information. Identify that LP problem solving in Cloud component is efficient extra cost on cloud server. In this paper we are utilizing Homomorphic encryption system to increase the performance and time efficiency.

Context-Aware Kubernetes Scheduler for Edge-native Applications on 5G

This paper is an extension of work originally presented in SoftCOM 2019 [1]. The novelty of this work reside in its focused improvement of our scheduling algorithm towards its usage on a real 5G infrastructure. Industrial IoT applications are often designed to run in a distributed way on the devices and controller computers with strict service requirements for the nodes and the links between them. 5G, especially in concomitance with Edge Computing, will provide the desired level of connectivity for these setups and it will permit to host application run-time components in edge clouds. However, allocation of the edge cloud resources for Industrial IoT (IIoT) applications, is still commonly solved by rudimentary scheduling techniques (i.e. simple strategies based on CPU usage and device readiness, employing very few dynamic information). Orchestrators inherited from the cloud computing, like Kubernetes, are not satisfying to the requirements of the aforementioned applications and are not optimized for the diversity of devices which are often also limited in capacity. This design is especially slow in reacting to the environmental changes. In such circumstances, in order to provide a proper solution using these tools, we propose to take the physical, operational and network parameters (thus the full context of the IIoT application) into consideration, along with the software states and orchestrate the applications dynamically.

A survey on security challenges in cloud computing: issues, threats, and solutions

Cloud computing has gained huge attention over the past decades because of continuously increasing demands. There are several advantages to organizations moving toward cloud-based data storage solutions. These include simplified IT infrastructure and management, remote access from effectively anywhere in the world with a stable Internet connection and the cost efficiencies that cloud computing can bring. The associated security and privacy challenges in cloud require further exploration. Researchers from academia, industry, and standards organizations have provided potential solutions to these challenges in the previously published studies. The narrative review presented in this survey provides cloud security issues and requirements, identified threats, and known vulnerabilities. In fact, this work aims to analyze the different components of cloud computing as well as present security and privacy problems that these systems face. Moreover, this work presents new classification of recent security solutions that exist in this area. Additionally, this survey introduced various types of security threats which are threatening cloud computing services and also discussed open issues and propose future directions. This paper will focus and explore a detailed knowledge about the security challenges that are faced by cloud entities such as cloud service provider, the data owner, and cloud user.

New insights in giant molecular cloud hosting the S147/S153 complex: signatures of interacting clouds

Abstract In order to understand the formation of massive OB stars, we report a multi-wavelength observational study of a giant molecular cloud hosting the S147/S153 complex (size ${\sim}90\:\:\mbox{pc} \times 50\:$pc). The selected complex is located in the Perseus arm, and contains at least five H ii regions (S147, S148, S149, S152, and S153) powered by massive OB stars with dynamical ages ${\sim}0.2$–$0.6\:$Myr. The Canadian Galactic Plane Survey $^{12}$CO line data (beam size ${\sim}{100{^{\prime \prime}_{.}}4}$) trace the complex in a velocity range of [$-59$, $-43]\:$km$\:$s$^{-1}$, and also reveal the presence of two molecular cloud components around $-54$ and $-49\:$km$\:$s$^{-1}$ in the direction of the complex. Signatures of the interaction/collision between these extended cloud components are investigated through their spatial and velocity connections. These outcomes suggest the collision of these molecular cloud components about $1.6\:$Myr ago. Based on the observed overlapping zones of the two clouds, the collision axis appears to be parallel to the line-of-sight. Deep near-infrared photometric analysis of point-like sources shows the distribution of infrared-excess sources in the direction of the overlapping zones of the molecular cloud components, where all the H ii regions are also spatially located. All elements put together, the birth of massive OB stars and embedded infrared-excess sources seems to be triggered by two colliding molecular clouds in the selected site. High-resolution observations of the dense gas tracer will be required to further confirm the proposed scenario.

Аналіз проблем безпеки IoT пристроїв

The paper considers the problems securing of protection of access to IoT-devices and their networks. A study by Microsoft experts, who found that the biggest problems in IoT networks are related to network-level security, was analyzed. The presence of a large number of insufficiently protected devices facilitates DDoS-attacks, in which home devices can be used to attack corporate systems. The main reason for the failure of IoT component manufacturers to implement security features is the high computational costs that reduce the service life of device batteries. HPE (Hewlett Packard Enterprise) experts recommend paying attention to both device owner issues and developer issues, and provide some tips on setting up IoT devices. HPE experts have identified about 25 different vulnerabilities in each of the studied devices (TVs, door locks, household scales, home security systems, electrical outlets, etc.) and their mobile and cloud components, compiled a list of major vulnerabilities and made a disappointing conclusion: a safe ecosystem IoT does not exist today. The statistics of attacks on IoT devices recorded during the last 2 years with the help of honeypots, collected by Kaspersky Lab specialists, are considered. It turned out that the number of attacks increases with the number of IP addresses. A list has been compiled of the countries with the highest number of attempted attacks. The largest number of attacks came from China and Brazil. The list of threats provided by Trend Micro is considered. The most common attack technologies, which can cause significant damage, are analyzed. Such technologies include: amplification, change of route information, selective mailing, bottomless funnel Sinkhole Attack, shamanic attack Sybil attack, wormhole attack Wormhole attack, flood attack HELLO flood attack. The list of basic recommendations for security in the IoT system is presented, namely: password protection, use of separate networks, refusal of automatic connection to the network. Keywords: Internet of Things (IoT), industrial devices of the Internet of Things (IIoT), inter-machine interaction (M2M), smart home, smart city, information security (IS), DDoS-attack.

Estimating capacity-oriented availability in cloud systems

Over the years, many companies have employed cloud computing to support their services and optimise their infrastructure utilisation. The provisioning of high availability and high processing capacity is a significant challenge when planning a cloud computing infrastructure. Even when the system is available, a part of the resources may not be offered due to partial failures in just a few of the many components in an IaaS cloud. The dynamic behaviour of virtualised resources requires special attention to the effective amount of capacity that is available to users, so the system can be correctly sized. Therefore, the estimation of capacity-oriented availability (COA) is an important activity for cloud infrastructure providers to analyse the cost-benefit tradeoff among distinct architectures and deployment sizes. This paper presents a strategy to evaluate the capacity-oriented availability of virtual machines combined to servers availability on a private cloud infrastructure. The proposed strategy aims to provide an efficient and accurate computation of COA and availability by means of closed-form equations. We compare our approach to the use of models such as continuous time Markov chains and SPN simulation model, considering execution time and values of metrics obtained with both approaches.

Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery

In earth observation systems, especially in the detection of small and weak targets, the detection and recognition of long-distance infrared targets plays a vital role in the military and civil fields. However, there are a large number of high radiation areas on the earth’s surface, in which cirrus clouds, as high radiation areas or abnormal objects, will interfere with the military early warning system. In order to improve the performance of the system and the accuracy of small target detection, the method proposed in this paper uses the suppression of the cirrus cloud as an auxiliary means of small target detection. An infrared image was modeled and decomposed into thin parts such as the cirrus cloud, noise and clutter, and low-order background parts. In order to describe the cirrus cloud more accurately, robust principal component analysis (RPCA) was used to get the sparse components of the cirrus cloud, and only the sparse components of infrared image were studied. The texture of the cirrus cloud was found to have fractal characteristics, and a random fractal based infrared image signal component dictionary was constructed. The k-cluster singular value decomposition (KSVD) dictionary was used to train the sparse representation of sparse components to detect cirrus clouds. Through the simulation test, it was found that the algorithm proposed in this paper performed better on the the receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve, had higher accuracy rate under the same recall rate, and its F-measure value and Intersection-over-Union (IOU) value were greater than other algorithms, which shows that it has better detection effect.

Resource Constrained Profit Optimization Method for Task Scheduling in Edge Cloud

Edge cloud is a cloud computing system built on edge infrastructure. Task scheduling optimization is the key technology to ensure the quality of service in edge cloud. However, the openness of the edge cloud environment challenges the load balancing and profit optimization of task scheduling. In this paper, we analyze the business process and optimization factors of task scheduling in edge cloud. First, we propose a resource constrained task scheduling profit optimization algorithm (RCTSPO), which consists of clustering preprocessing, classification, profit matrix construction and optimal scheduling strategy calculation. Clustering preprocessing gathers similar tasks into one class and perform a classification on the clustered tasks. Then construct the profit matrix for resource constrained task scheduling, and the optimal task scheduling strategy is obtained based on the constructed profit matrix. Second, Petri nets are used to construct the different components of edge cloud, such as resource, task, user request and virtual machine, thus forming the task scheduling model of edge cloud. Third, the properties of task scheduling model are verified by using the related theory and tools of Petri nets. Finally, several experiments are done to evaluate the proposed method, the simulation results show that the algorithm not only achieves the maximum profit, but also performs well in terms of time, reliability and load balancing of task scheduling.

Artificial Intelligence (AI) in cloud integrated, open access pathology publications: perspectives on 2020.

Goal: To describe, maintain and further develop the communication network of medical sciences www.diagnosticpathology.eu that publishes electronically submitted peer reviewed medical articles and fully takes advantage of its electronic environment, and to give the reader the opportunity to view digitized whole slide images (virtual slides, VS), to measure image objects, and to annotate images and text.Background: The unique open access, peer reviewed journal www.diagnosticpathology.eu is embedded in a communication environment of different cloud components. The components include several distributed servers and databank systems such as access to article integrated VS, data storage, or scientific data banks (atlas of fine granulate and natural and synthetic fiber hazards). Implementation specificities: Theoretical considerations on specific substantial differences between the physical real world and its virtual transformations guide the implementation. The differences include, for example, the minimum number of mandatory space dimensions, of their essential (ir)-reversibility of objects, structures, and functions as well as the relationship of image features to the observation time.The implemented system focuses on communication issues in tissue – based science only. Its volunteers allow disregard any predominantly financial impact such as financial profit. Artificial intelligence (AI) is used to maintain its sustainability, connectivity, distribution, measurement and discussion of medical images, especially microscopic slides. 2019 Data: The journal and its concurrent operation of interactive communication demonstrate the advantage of AI in open access publication. VS are ready to be screened and annotated by any reader worldwide. QR codes provide DOI registration and upload of oral presentations by the auditorium. Interactive publication permits the release of a distinct continuous article chain. Annotations of VS images can be transferred in public or private databanks. The reader is invited to check his impression of marker scores by own automated measurements.Perspectives: Applications of AI in tissue – based diagnosis, communication and implementation are not limited to deep learning, quality assurance or so-called diagnosis assistants. AI is on the way to significantly modify medical diagnostics and treatment. These modifications will, in addition, modify our understanding of disease and life. www.diagnosticpathology.eu is one of the pathfinders and pioneers in this unavoidable process.Keywords: Artificial Intelligence, Open Access Journal, Virtual Slide, Deep Learning, Tissue – based Diagnosis. Virtual Slides: Tuberculosis Fibrosis The exemplarily included virtual slides and AVI Videos are published in http://dx.doi.org/10.17629/www.diagnosticpathology.eu-2018-4:269

Key Metrics Identification of Distinct Process Models to Develop an Iot Based Systems

The globe is encompassing gravitate automation. Many organizations turning the theoretical aspects into practical implementation by converging technologies. Embedded systems considered the core of sensor-based technology. Bundle of built-in configuration required to interact devices. Productivity software confederate to communicate with the integrated systems over the web. Process models furnished strategies to implement systems. Process models like a classical waterfall, Spiral, XP, Agile, RUP, Prototype, and Scrum which provides the blueprint for developers. The paper investigating the variations among process models and integrated system development. Discrete factors have to be considered an excerpt of the process model to an integrated system. Process models can be suitable to implement small scale and medium scale systems but implementing sophisticated systems deals with various critical factors. The non-functional attributes of development tools and deployment environments are the key components in system development. Since the cloud is the component of IoT and cloud providing solutions as per pay and use policy. Identifying process model for IoT systems depends on application nature, development and deployment environments. IoT systems require real-time operating systems or devices to interpret code in it. The probable scrutiny is given information about the agile methodology. The research involving with identification of key metrics of various process models to develop IoT based systems. The paper presuming the external factors not involving with the process mode. Identified key factors from agile model to develop medium level integrated systems.

Changes in the Soluble and Insoluble Compounds of Shelf-Stable Orange Juice in Relation to Non-Enzymatic Browning during Storage.

For the first time in literature, this study revealed the participation of polymeric components of orange juice cloud and pulp (such as proteins, arabinogalactan proteins, or protein-pectin complexes) during nonenzymatic browning. In a quest to better understand the nonenzymatic browning of shelf-stable orange juice during storage, the juice was fractionated into different fractions depending on the solubility in water/ethanol and the obtained fractions were characterized. The results showed that brown compounds that were formed during storage of orange juice were distributed over water insoluble (pulp), ethanol insoluble (cloud), and ethanol soluble (serum) fractions. In the ethanol insoluble fraction, the brown compounds are hypothesized to be associated with proteins, arabinogalactan proteins, and/or protein-pectin complexes of this fraction without significantly changing their molecular weight distributions, monosaccharide compositions, and protein contents. The changes in the ethanol soluble fraction including ascorbic acid degradation, acid-catalyzed hydrolysis of sucrose, and formation of furfural and 5-hydroxymethylfurfural were highly correlated to the browning development of the juice during storage.

Cloud virtual machine lifecycle security framework based on trusted computing

As a foundation component of cloud computing platforms, Virtual Machines (VMs) are confronted with numerous security threats. However, existing solutions tend to focus on solving threats in a specific state of the VM. In this paper, we propose a novel VM lifecycle security protection framework based on trusted computing to solve the security threats to VMs throughout their entire lifecycle. Specifically, a concept of the VM lifecycle is presented divided up by the different active conditions of the VM. Then, a trusted computing based security protection framework is developed, which can extend the trusted relationship from trusted platform module to the VM and protect the security and reliability of the VM throughout its lifecycle. The theoretical analysis shows that our proposed framework can provide comprehensive safety to VM in all of its states. Furthermore, experiment results demonstrate that the proposed framework is feasible and achieves a higher level of security compared with some state-of-the-art schemes.

SIV: a structural integrity verification approach of cloud components with enhanced privacy

Private data leakage is a threat to current integrity verification schemes of cloud components. To address this issue, this work proposes a privacy-enhancing Structural Integrity Verification (SIV) approach. It is made up of three processes: proof organization, proof transformation, and integrity judgement. By introducing a Merkle tree technique, the integrity of a constituent part of a cloud component on a node is represented by a root value. The value is then masked to cipher texts in proof transformation. With the masked proofs, a structural feature is extracted and validated in an integrity judgement by a third-party verification provider. The integrity of the cloud component is visually displayed in the output result matrix. If there are abnormities, the corrupted constituent parts can be located. Integrity is verified through the encrypted masked proofs. All raw proofs containing sensitive information stay on their original nodes, thus minimizing the attack surface of the proof data, and eliminating the risk of leaking private data at the source. Although some computations are added, the experimental results show that the time overhead is within acceptable bounds.

Using Deep Learning and Mobile Offloading to Control a 3D-printed Prosthetic Hand

Although many children are born with congenital limb malformation, contemporary functional artificial hands are costly and are not meant to be adapted to growing hand. In this work, we develop a low cost, adaptable and personalizable system of an artificial prosthetic hand accompanied with hardware and software modules. Our solution consists of (i) a consumer grade electromyography (EMG) recording hardware, (ii) a mobile companion device empowered by deep learning classification algorithms, (iii) an cloud component for offloading computations, and (iv) mechanical 3D printed arm operated by the embedded hardware. We focus on the flexibility of the designed system making it more affordable than the alternatives. We use 3D printed materials and open-source software thus enabling the community to contribute and improve the system. In this paper, we describe the proposed system and its components and present the experiments we conducted in order to show the feasibility and applicability of our approach. Extended experimentation shows that our proposal is energy efficient and has high accuracy.

A diagnosis of the interannual variation of the summer hydrometeor based on ERA-interim over Eastern China

Hydrometeor, which involves precipitation and cloud water net income, is the direct source of precipitation and the main component of cloud. It plays an important role in the climate system. In this study, we analyzed the climatology and interannual variation of the summer hydrometeor over Eastern China during 1979–2016 using ERA-Interim reanalysis datasets. The study region was divided into southern and northern sub-regions. Moreover, the hydrometeor budget analysis was diagnosed to further shed light on the formation and variation of the summer hydrometeor. The results showed that the summer hydrometeor pattern is more over the south and less over the north. In climatology, the hydrometeor is mainly contributed by evaporation and vertical moisture advection over the southern sub-region, while the most significant two contributions are evaporation and horizontal moisture advection over the northern sub-region. The interannual variation of hydrometeor over Eastern China exhibits the “north-south seesaw” pattern, which is mainly caused by atmospheric vertical motion. The hydrometeor interannual variation depends strongly on the vertical and horizontal moisture advections over the northern sub-region, but it is primarily contributed by vertical moisture advection over the southern sub-region. The anomalous anticyclone located over the southeast coast of China - the northwest Pacific is in favor of the northward shift of the Western Pacific Subtropical High (WPSH), leading to the hydrometeor increase over the northern sub-region and the opposite over the southern sub-region. It indicates that the hydrometeor distribution pattern over Eastern China is mainly regulated by the WPSH. Further analysis indicated that the interannual variation of horizontal moisture advection over the northern area and vertical moisture advection are mainly caused by the change of the dynamic component, while the horizontal moisture advection over the southern area has both dynamic and thermodynamic drives, which offset each other.

Extracting Diameter at Breast Height with a Handheld Mobile LiDAR System in an Outdoor Environment.

Mobile laser scanning (MLS) is widely used in the mapping of forest environments. It has become important for extracting the parameters of forest trees using the generated environmental map. In this study, a three-dimensional point cloud map of a forest area was generated by using the Velodyne VLP-16 LiDAR system, so as to extract the diameter at breast height (DBH) of individual trees. The Velodyne VLP-16 LiDAR system and inertial measurement units (IMU) were used to construct a mobile measurement platform for generating 3D point cloud maps for forest areas. The 3D point cloud map in the forest area was processed offline, and the ground point cloud was removed by the random sample consensus (RANSAC) algorithm. The trees in the experimental area were segmented by the European clustering algorithm, and the DBH component of the tree point cloud was extracted and projected onto a 2D plane, fitting the DBH of the trees using the RANSAC algorithm in the plane. A three-dimensional point cloud map of 71 trees was generated in the experimental area, and estimated the DBH. The mean and variance of the absolute error were 0.43 cm and 0.50, respectively. The relative error of the whole was 2.27%, the corresponding variance was 15.09, and the root mean square error (RMSE) was 0.70 cm. The experimental results were good and met the requirements of forestry mapping, and the application value and significance were presented.