基于Depth-wise卷积和视觉Transformer的图像分类模型

图像分类作为一种常见的视觉识别任务,有着广阔的应用场景。在处理图像分类问题时,传统的方法通常使用卷积神经网络,然而,卷积网络的感受野有限,难以建模图像的全局关系表示,导致分类精度低,难以处理复杂多样的图像数据。为了对全局关系进行建模,一些研究者将Transformer应用于图像分类任务,但为了满足Transformer的序列化和并行化要求,需要将图像分割成大小相等、互不重叠的图像块,破坏了相邻图像数据块之间的局部信息。此外,由于Transformer具有较少的先验知识,模型往往需要在大规模数据集上进行预训练,因此计算复杂度较高。为了同时建模图像相邻块之间的局部信息并充分利用图像的全局信息,提出了一种基于Depth-wise卷积的视觉Transformer(Efficient Pyramid Vision Transformer,EPVT)模型。EPVT模型可以实现以较低的计算成本提取相邻图像块之间的局部和全局信息。EPVT模型主要包含3个关键组件:局部感知模块(Local Perceptron Module,LPM)、空间信息融合模块(Spatial Information Fusion,SIF)和“+卷积前馈神经网络(Convolution Feed-forward Network,CFFN)。LPM模块用于捕获图像的局部相关性;SIF模块用于融合相邻图像块之间的局部信息,并利用不同图像块之间的远距离依赖关系,提升模型的特征表达能力,使模型学习到输出特征在不同维度下的语义信息;CFFN模块用于编码位置信息和重塑张量。在图像分类数据集ImageNet-1K上,所提模型优于现有的同等规模的视觉Transformer分类模型,取得了82.6%的分类准确度,证明了该模型在大规模数据集上具有竞争力。

Perpendicular-Cutdepth:Perpendicular Direction Depth Cutting Data Augmentation Method

Depth estimation is an important task in computer vision.Collecting data at scale for monocular depth estimation is challenging,as this task requires simultaneously capturing RGB images and depth information.Therefore,data augmentation is crucial for this task.Existing data augmentationmethods often employ pixel-wise transformations,whichmay inadvertently disrupt edge features.In this paper,we propose a data augmentationmethod formonocular depth estimation,which we refer to as the Perpendicular-Cutdepth method.This method involves cutting realworld depth maps along perpendicular directions and pasting them onto input images,thereby diversifying the data without compromising edge features.To validate the effectiveness of the algorithm,we compared it with existing convolutional neural network(CNN)against the current mainstream data augmentation algorithms.Additionally,to verify the algorithm’s applicability to Transformer networks,we designed an encoder-decoder network structure based on Transformer to assess the generalization of our proposed algorithm.Experimental results demonstrate that,in the field of monocular depth estimation,our proposed method,Perpendicular-Cutdepth,outperforms traditional data augmentationmethods.On the indoor dataset NYU,our method increases accuracy from0.900 to 0.907 and reduces the error rate from0.357 to 0.351.On the outdoor dataset KITTI,our method improves accuracy from 0.9638 to 0.9642 and decreases the error rate from 0.060 to 0.0598.

What induced the trend shift of mixed-layer depths in the Antarctic Circumpolar Current region in the mid-1980s?

An obvious trend shift in the annual mean and winter mixed layer depth(MLD)in the Antarctic Circumpolar Current(ACC)region was detected during the 1960–2021 period.Shallowing trends stopped in mid-1980s,followed by a period of weak trends.The MLD deepening trend difference between the two periods were mainly distributed in the western areas in the Drake Passage,the areas north to Victoria Land and Wilkes Land,and the central parts of the South Indian sector.The newly formed ocean current shear due to the meridional shift of the ACC flow axis between the two periods is the dominant driver for the MLD trends shift distributed in the western areas in the Drake Passage and the central parts of the South Indian sector.The saltier trends in the regions north to Victoria Land and Wilkes Land could be responsible for the strengthening mixing processes in this region.

Three-dimensional constrained gravity inversion of Moho depth and crustal structural characteristics at Mozambique continental margin

Mozambique's continental margin in East Africa was formed during the break-off stage of the east and west Gondwana lands. Studying the geological structure and division of continent-ocean boundary(COB) in Mozambique's continental margin is considered of great significance to rebuild Gondwana land and understand its movement mode. Along these lines, in this work, the initial Moho was fit using the known Moho depth from reflection seismic profiles, and a 3D multi-point constrained gravity inversion was carried out. Thus, highaccuracy Moho depth and crustal thickness in the study area were acquired. According to the crustal structure distribution based on the inversion results, the continental crust at the narrowest position of the Mozambique Channel was detected. According to the analysis of the crustal thickness, the Mozambique ridge is generally oceanic crust and the COB of the whole Mozambique continental margin is divided.

DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection

We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations.Initially,to enhance the capability of deep neural networks in extracting geometric attributes from depth images,we developed a novel deep geometric convolution operator(DGConv).DGConv is utilized to construct a deep local geometric feature extraction module,facilitating a more comprehensive exploration of the intrinsic geometric information within depth images.Secondly,we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network(FCN8)to establish a high-performance deep neural network algorithm tailored for depth image segmentation.Concurrently,we enhance the FCN8 detection head by separating the segmentation and classification processes.This enhancement significantly boosts the network’s overall detection capability.Thirdly,for a comprehensive assessment of our proposed algorithm and its applicability in real-world industrial settings,we curated a line-scan image dataset featuring weld seams.This dataset,named the Standardized Linear Depth Profile(SLDP)dataset,was collected from actual industrial sites where autonomous robots are in operation.Ultimately,we conducted experiments utilizing the SLDP dataset,achieving an average accuracy of 92.7%.Our proposed approach exhibited a remarkable performance improvement over the prior method on the identical dataset.Moreover,we have successfully deployed the proposed algorithm in genuine industrial environments,fulfilling the prerequisites of unmanned robot operations.

Applying Source Parameter Imaging (SPI) to Aeromagnetic Data to Estimate Depth to Magnetic Sources in the Mamfe Sedimentary Basin

Aeromagnetic data over the Mamfe Basin have been processed. A regional magnetic gridded dataset was obtained from the Total Magnetic Intensity (TMI) data grid using a 3 × 3 convolution (Hanning) filter to remove regional trends. Major similarities in magnetic field orientation and intensities were observed at identical locations on both the regional and TMI data grids. From the regional and TMI gridded datasets, the residual dataset was generated which represents the very shallow geological features of the basin. Processing this residual data grid using the Source Parameter Imaging (SPI) for magnetic depth suggests that the estimated depths to magnetic sources in the basin range from about 271 m to 3552 m. The highest depths are located in two main locations somewhere around the central portion of the study area which correspond to the area with positive magnetic susceptibilities, as well as the areas extending outwards across the eastern boundary of the study area. Shallow magnetic depths are prominent towards the NW portion of the basin and also correspond to areas of negative magnetic susceptibilities. The basin generally exhibits a variation in depth of magnetic sources with high, average and shallow depths. The presence of intrusive igneous rocks was also observed in this basin. This characteristic is a pointer to the existence of geologic resources of interest for exploration in the basin.

Spatiotemporal Changes of Snow Depth in Western Jilin,China from 1987 to 2018

Seasonal snow cover is a key global climate and hydrological system component drawing considerable attention due to glob-al warming conditions.However,the spatiotemporal snow cover patterns are challenging in western Jilin,China due to natural condi-tions and sparse observation.Hence,this study investigated the spatiotemporal patterns of snow cover using fine-resolution passive mi-crowave(PMW)snow depth(SD)data from 1987 to 2018,and revealed the potential influence of climate factors on SD variations.The results indicated that the interannual range of SD was between 2.90 cm and 9.60 cm during the snowy winter seasons and the annual mean SD showed a slightly increasing trend(P>0.05)at a rate of 0.009 cm/yr.In snowmelt periods,the snow cover contributed to an increase in volumetric soil water,and the change in SD was significantly affected by air temperature.The correlation between SD and air temperature was negative,while the correlation between SD and precipitation was positive during December and March.In March,the correlation coefficient exceeded 0.5 in Zhenlai,Da’an,Qianan,and Qianguo counties.However,the SD and precipitation were neg-atively correlated over western Jilin in October,and several subregions presented a negative correlation between SD and precipitation in November and April.

Improving path planning efficiency for underwater gravity-aided navigation based on a new depth sorting fast search algorithm

This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.

基于Boosting-Monodepth的管道病害深度估计与三维重建

城市地下管道是城市的血脉经络,但随着排水管道的大量投入运营和使用年限增加,引发了一系列的管道病害安全隐患,如管道整体结构变形、内表面破裂和管中异物插入等问题,传统的病害图像视频采集、检测和后期病害分类甄选都是从二维视角出发,欠缺对三维空间信息(深度)的考虑。针对上述3种病害从生成深度图、由二维深度图重建三维管道病害这两方面进行研究,提出了一种基于boosting-monodepth的双重深度估计方法以提升深度图效果,最终生成画面连续一致、轮廓清晰的深度图。性能评估方面采用Abs-Rel、RMSE、SqRel、ORD和D3R等通用指标,与传统算法对比,结果显示boosting-monodepth的RMSE值降低了30%,精确度指标δ<1.25时,模型深度信息预测精确度提高了18%,此后以得到的深度图为基础重建管道病害三维点云,并在CloudCompare软件上三维可视化,最后采用随机采样一致算法测算病害深度并和实测数据对比证明其有效性和准确性。

Determination of minimum overburden depth for underwater shield tunnel in sands:Comparison between circular and rectangular tunnels

With the development of global urbanization,the utilization of underground space is more critical and attractive for civil purposes.Various shapes of shield tunnels have been gradually proposed to cope with different geological conditions and service purposes of underground structures.Generally,reducing the burial depth of shield tunnel is conducive to construction and cost saving.However,extremely small overburden depth cannot provide sufficient uplift resistance to maintain the stability and serviceability of the tunnel.To this end,this paper firstly reviewed the status of deriving the minimum sand over-burden depth of circular shield tunnel using mechanical equilibrium(ME)method.It revealed that the estimated depth is rather conservative.Then,the uplift resistance mechanism of both circular and rectangular tunnels was deduced theoretically and verified with the model tests.The theoretical uplift resistance is consistent with the experimental values,indicating the feasibility of the proposed equations.Furthermore,the determination of the minimum soil overburden depth of rectangular shield tunnel under various working conditions was presented through integrated ME method,which can provide more reasonable estimations of suggested tunnel burial depth for practical construction.Additionally,optimizations were made for calculating the uplift resistance,and the soil thickness providing uplift resistance is suggested to be adjusted according to the testing results.The results can provide reference for the design and construction of various shapes of shield tunnels in urban underground space exploitation.

Fusion of color and hallucinated depth features for enhanced multimodal deep learning-based damage segmentation

Recent advances in computer vision and deep learning have shown that the fusion of depth information can significantly enhance the performance of RGB-based damage detection and segmentation models.However,alongside the advantages,depth-sensing also presents many practical challenges.For instance,the depth sensors impose an additional payload burden on the robotic inspection platforms limiting the operation time and increasing the inspection cost.Additionally,some lidar-based depth sensors have poor outdoor performance due to sunlight contamination during the daytime.In this context,this study investigates the feasibility of abolishing depth-sensing at test time without compromising the segmentation performance.An autonomous damage segmentation framework is developed,based on recent advancements in vision-based multi-modal sensing such as modality hallucination(MH)and monocular depth estimation(MDE),which require depth data only during the model training.At the time of deployment,depth data becomes expendable as it can be simulated from the corresponding RGB frames.This makes it possible to reap the benefits of depth fusion without any depth perception per se.This study explored two different depth encoding techniques and three different fusion strategies in addition to a baseline RGB-based model.The proposed approach is validated on computer-generated RGB-D data of reinforced concrete buildings subjected to seismic damage.It was observed that the surrogate techniques can increase the segmentation IoU by up to 20.1%with a negligible increase in the computation cost.Overall,this study is believed to make a positive contribution to enhancing the resilience of critical civil infrastructure.

Considering explosive charge shape and embedded depth in the design of concrete shelter thickness

Cost and safety are important considerations when designing the thickness of a protective reinforced concrete shelter.The blast perforation limit(BPL)is the minimum concrete shelter thickness that resists perforation under blast loading.To investigate the influence of the depth of embedment(DOE)and length-to-diameter ratio(L/D)of an explosive charge on the BPL,the results of an explosion test using a slender explosive partially embedded in a reinforced concrete slab were used to validate a refined finite element model.This model was then applied to conduct more than 300 simulations with strictly controlled variables,obtaining the BPLs for various concrete slabs subjected to charge DOEs ranging from0 to∞and L/D values ranging from 0.89 to 6.87.The numerical results were compared with the experimental results from published literature,further verifying the reliability of the simulation.The findings indicate that for the same explosive charge mass and L/D,the greater the DOE,the larger the critical residual thickness(Rc,defined as the difference between the BPL and DOE)up to a certain constant value;for the same explosive charge mass and DOE,the greater the L/D,the smaller the Rc.Thus,corresponding DOE and shape coefficients were introduced to derive a new equation for the BPL,providing a theoretical approach to the design and safety assessment of protective structures.

Experimental investigation of the impact of water depth, inlet water temperature, and fins on the productivity of a Pyramid Solar Still

This experimental study aimed to investigate the impact of water depth, inlet water temperature,and fins on the productivity of a pyramid solar still in producing distilled water. The experiment was conducted in three parts, where the first part explored the variation in water depth from 1 cm to 5 cm, the second part evaluated the effect of increasing inlet water temperature from 30℃ to 50℃, and the third part added fins at the bottom of the still at a specific inlet water depth. Results showed that basin depth had a significant impact on the still's production, with a maximum variation of 40.6% observed when the water level was changed from 1 cm to 5 cm. The daily freshwater production from the pyramid solar still ranged from 3.41 kg/m~2 for a water depth of 1 cm to 2.02 kg/m~2 for a depth of 5 cm. Adding fins at the bottom of the pyramid solar still led to a 7.5% increase in productivity, while adjusting the inlet water temperature from 30℃ to 40℃ and 50℃ resulted in a 15.3% and 21.2% increase, respectively. These findings highlighted the essential factors that can influence the productivity of pyramid solar stills and can be valuable in designing and operating efficient water desalination and purification technologies.

Measuring tree stem diameters and straightness with depth-image computer vision

Current techniques of forest inventory rely on manual measurements and are slow and labor intensive.Recent developments in computer vision and depth sensing can produce accurate measurement data at significantly reduced time and labor costs.We developed the ForSense system to measure the diameters of trees at various points along the stem as well as stem straightness.Time use,mean absolute error(MAE),and root mean squared error(RMSE)metrics were used to compare the system against manual methods,and to compare the system against itself(reproducibility).Depth-derived diameter measurements of the stems at the heights of 0.3,1.4,and 2.7 m achieved RMSE of 1.7,1.5,and 2.7 cm,respectively.The ForSense system produced straightness measurement data that was highly correlated with straightness ratings by trained foresters.The ForSense system was also consistent,achieving sub-centimeter diameter difference with subsequent measures and less than 4%difference in straightness value between runs.This method of forest inventory,which is based on depth-image computer vision,is time efficient compared to manual methods and less computationally and technologically intensive compared to Structure-from-Motion(SFM)photogrammetry and ground-based LiDAR or terrestrial laser scanning(TLS).

Depth profiling of arsenian pyrite in Carlin-type ores through wet chemistry

Enrichment of As and Au at the overgrowth rims of arsenian pyrite is a distinctive feature of Carlin-type gold ores.Revealing distribution of such key elements in high resolution is of fundamental importance yet often proves challenging.In this study,repeated non-oxidative acid etching of ore samples from Shuiyindong gold deposit was applied to enable elemental depth profiling of goldbearing arsenian pyrite grains.ICP-OES and AAS were used to determine the dissolved Fe,As,and Au concentrations in each of the etching solutions,and XPS was carried out to exam the etched mineral surfaces.In contrast to conventional ion beam etching that may cause substantial sample damage,our acid etching method does not seem to significantly alter the composition and chemical state of the samples.The etched depths directly converted from the measured elemental concentrations can reproducibly reach a very high resolution of~1 nm,and can be conveniently controlled through varying the etching time.While the Fe and As depth profiles consistently reflect the surface oxidation property of arsenian pyrite,the Au profile displaying an obvious upward trend reveals the ore fluid evolution at the late stage of mineralization.Based on our experimental results,we demonstrate that our wet chemistry method is capable of effective depth profiling of gold ore and perhaps other geological samples,with advantages surpassing many instrumental techniques including negligible sample damage,nanoscale resolution as well as isotropic etching.

Changes in Aerosol Optical Depth over the Arctic Ocean as Seen by CALIOP, MAIAC, and MODIS C6.1

Due to the recent increase in Arctic shipping, 2006-2020 June to October Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1), and Multi-Angle Implementation of Atmospheric Correction (MAIAC) retrieved aerosol optical depth (AOD) data were examined for changes in AOD from period 1 (P1, 2006-2012) to period 2 (P2, 2014-2020 (P2). Herein, AOD was statistically analyzed on a 0.25° × 0.25° grid and in the airsheds over the various ocean basins over the Arctic north of 59.75°N. According to heatmaps of the correlation between AOD and ship traffic, and AOD and fire emissions for the airsheds, all three AOD products captured the observed inter-annual variability in wildfire occurrence well, and showed wildfire emissions over Siberia were more severe in P2 than P1. Except for the Atlantic, North, and Baltic Seas, Beaufort Sea, and Barents Sea, all three AOD products indicated that AOD was higher over the various basins in P2 than P1, but disagreed on the magnitude. This fact suggests that the detection of changes in the typical low AOD over the Arctic Ocean might be rather qualitative than quantitative. While all products captured increases in AOD due to ships at berth, only MODIS C6.1 caught the elevated AOD due to shipping on the Siberian rivers. Obviously, sub-daily resolutions are required to capture increased AOD due to short-term events like a traveling ship or short-interval fire.

Retrieval of snow depth on Antarctic sea ice from the FY-3D MWRI data

The snow depth on sea ice is an extremely critical part of the cryosphere.Monitoring and understanding changes of snow depth on Antarctic sea ice is beneficial for research on sea ice and global climate change.The Microwave Radiation Imager(MWRI)sensor aboard the Chinese FengYun-3D(FY-3D)satellite has great potential for obtaining information of the spatial and temporal distribution of snow depth on the sea ice.By comparing in-situ snow depth measurements during the 35th Chinese Antarctic Research Expedition(CHINARE-35),we took advantage of the combination of multiple gradient ratio(GR(36V,10V)and GR(36V,18V))derived from the measured brightness temperature of FY-3D MWRI to estimate the snow depth.This method could simultaneously introduce the advantages of high and low GR in the snow depth retrieval model and perform well in both deep and shallow snow layers.Based on this,we constructed a novel model to retrieve the FY-3D MWRI snow depth on Antarctic sea ice.The new model validated by the ship-based observational snow depth data from CHINARE-35 and the snow depth measured by snow buoys from the Alfred Wegener Institute(AWI)suggest that the model proposed in this study performs better than traditional models,with root mean square deviations(RMSDs)of 8.59 cm and 7.71 cm,respectively.A comparison with the snow depth measured from Operation IceBridge(OIB)project indicates that FY-3D MWRI snow depth was more accurate than the released snow depth product from the U.S.National Snow and Ice Data Center(NSIDC)and the National Tibetan Plateau Data Center(NTPDC).The spatial distribution of the snow depth from FY-3D MWRI agrees basically with that from ICESat-2;this demonstrates its reliability for estimating Antarctic snow depth,and thus has great potential for understanding snow depth variations on Antarctic sea ice in the context of global climate change.

Depth Map Prediction of Occluded Objects Using Structure Tensor with Gain Regularization

The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map.3D modeling and view synthesis algorithms could effectively handle the obtained disparity maps.This work uses the consistency check method to find an accurate depth map for identifying occluded pixels.The prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for evaluation.The improved depth map quality within a reasonable process-ing time outperforms the other existing depth map prediction algorithms.The experimental results have shown that the proposed depth map predictioncould identify the inter-object boundaryeven with the presence ofocclusion with less detection error and runtime.We observed that the Middlebury stereo dataset has very few images with occluded objects,which made the attainment of gain cumbersome.Considering this gain,we have created our dataset with occlu-sion using the structured lighting technique.The proposed regularization term as an optimization process in the graph cut algorithm handles occlusion for different smoothing coefficients.The experimented results demonstrated that our dataset had outperformed the Tsukuba dataset regarding the percentage of occluded pixels.

Effects of Tillage Depth on Nutrients and Microbial Communities in Tobacco-Planting Soil

The implementation of appropriate tillage practices is of great significance for agricultural production. However, the effects of different tillage depths on soil nutrients content and microbial communities in tobacco-planting soils are still lacking systematic research. In this study, three different tillage depths of 15 cm (T1), 20 cm (T2), and 30 cm (T3) were set up for field experiments in Liupanshui, Guizhou Province, to explore the effects of tillage depth on tobacco-planting soil nutrients and bacterial and fungal communities based on 16S rRNA and ITS sequencing and figure out the key factors affecting soil microbial communities. The results showed that T2 and T3 increased the contents of organic matter, total nitrogen, total phosphorus, available phosphorus, and available potassium in tobacco-planting soil, and increased the diversity of bacterial communities compared with T1. There was no significant difference in the structure of bacterial and fungal communities in different tillage depth treatments, but some dominant genera were significantly enriched in T2 and T3. Desulfobacter, Setophoma, Humicola, and Acremonium were significantly enriched in T2. Chthonomonas and Fusarium were significantly enriched in T3. These genera favor the decomposition of organic matter and the cycling of nutrients, and control soil pests and diseases. Redundancy analysis indicated that TP and AK were the key factors influencing the dominant genera of bacteria and fungi. This study provides a scientific basis for the selection of soil tillage depth for tobacco production in this region.

Verification of Depth Adjustment Factor in Eurocode 5 for Tropical Hardwood Timbers

The depth adjustment factor for bending strength stated in Eurocode 5(EC5)is only applicable to timbers having a characteristic density below 700 kg/m^(3).However,most Malaysian timbers are hardwood,some with a characteristic density reaching above 700 kg/m^(3).Therefore,the objective of this study was to examine whether the depth adjustment factor stipulated in EC5 is valid for Malaysian hardwood timbers.Six timber species were selected for this study,namely Kapur(Dryobalanops C.F.Gaertn.),Kempas(Koompassia Maingay ex Benth.),Keruing(Dipterocarpus C.F.Gaertn.),Light red meranti(Shorea Roxb.ex C.F.Gaertn.),Geronggang(Cratoxylum Blume)and Balau(Shorea Roxb.ex C.F.Gaertn.).The determination of bending strength and characteristic density was conducted according to BS EN 408:2010 and BS EN 384:2016,respectively.A graph for mean bending strength vs.(150/h)was plotted for each timber species.The power function was selected to analyze the relationship between the two variables.The power of the regression equations varied depending on the characteristic density of the timber species.For species with a characteristic density below 700 kg/m^(3),such as Kapur,Keruing,and Light red meranti,the power was between 0.16 to 0.17.In contrast,for species having a characteristic density above 700 kg/m^(3),namely Kempas and Balau,the power was higher at 0.23 and 0.24,respectively.Geronggang was an exception to this pattern.These values are close to the depth adjustment factor given in EC5,which is 0.2.Based on the results,it can be suggested that the adjustment factor of 0.2 is also applicable to Malaysian hardwood timbers with a characteristic density above 700 kg/m^(3).