Etienne Costa-Patry^a, Jonathan Olivier^b, John R. Thome^a,∗

Frontiers in Heat and Mass Transfer, Vol.3, No.1, pp. 1-14, 2012, DOI:10.5098/hmt.v3.1.3002

Abstract This article presents new experimental results for two-phase flow boiling of R-134a, R-1234ze(E) and R-245fa in a micro-evaporator. The test section was made of copper and composed of 52 microchannels 163μm wide and 1560μm high with the channels separated by 178μm wide fins. The channels were 13.2mm long. There were 35 local heaters and temperature measurements arranged in a 5×7 array as a pseudo-CPU. The total pressure drops of the test section were below 20kPa in all cases. The wall heat transfer coefficients were generally above 10’000W/m²K and a function of the heat flux, vapor quality and mass flux. A… More >

Tomio Okawa^*,†

Frontiers in Heat and Mass Transfer, Vol.3, No.1, pp. 1-8, 2012, DOI:10.5098/hmt.v3.1.3001

Abstract The present article summarizes the studies on the thermalhydraulic condition under which the onset of nucleate boiling (ONB) is triggered in subcooled flow boiling. Available correlations and experimental data show that the ONB is tended to be delayed in mini and microchannels. It is known that the ONB condition is significantly dependent on the surface condition even in conventional-sized channels. Accumulation of ONB data accompanied by the information on the surface condition is therefore considered of importance to elucidate the mechanisms of boiling incipience in microchannels. Discussion is also made for the bubble dynamics observed in mini and microchannels. It… More >

Frontiers in Heat and Mass Transfer, Vol.3, No.1, pp. 1-3, 2012, DOI:10.5098/hmt.v3.1.1001

Abstract This article has no abstract. More >

Md. Farhad Ismail^a,*, M.A.I. Rashid^b , M. Mahbub^b

Frontiers in Heat and Mass Transfer, Vol.3, No.3, pp. 1-8, 2012, DOI:10.5098/hmt.v3.3.3002

Abstract Carbon nanotube (CNT) has been proven to be an effective material for the thermal management of MEMS-based devices due to its superior thermal conductivity. At the same time, micro-channel heat-sinks are widely used in electronic products as a high performance heat transfer device because of its simple construction, easy fabrication process and effective heat removal capability. A numerical study has been carried out to investigate the thermal-fluid characteristics of the aligned and staggered MWCNT (multi walled CNT) based micro pin fins having 650 µm long with hydraulic diameter of ~130 µm. Average heat transfer coefficients have been obtained for effective… More >

Soumei Baba^a,*, Nobuo Ohtani^a, Osamu Kawanami^b, Koichi Inoue^c, Haruhiko Ohta^a

Frontiers in Heat and Mass Transfer, Vol.3, No.4, pp. 1-8, 2012, DOI:10.5098/hmt.v3.4.3002

Abstract Effects of tube orientation on flow boiling heat transfer coefficients were investigated for FC72 flowing in single mini-tubes with tube diameters of 0.13 and 0.51 mm to define boundaries on a dominant force regime map. For the tube diameter of 0.51 mm, when mass velocity and vapor quality was varied, heat transfer coefficients were influenced by tube orientation at Froude number Fr < 4, while the effect of tube orientation on heat transfer coefficients disappears at Fr > 4. The results indicated that the boundary between the body force dominated and the inertia dominated regimes was given by Fr ≈… More >

Yanmei Li^1,*, Jinghong Tang¹, Weiwu Ding¹, Jian Luo², Naveed Ahmad³, Rajesh Kumar⁴

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 713-733, 2024, DOI:10.32604/cmc.2023.046872

Abstract Alzheimer’s disease (AD) is a complex, progressive neurodegenerative disorder. The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice. In this study, we introduce an advanced diagnostic methodology rooted in the Med-3D transfer model and enhanced with an attention mechanism. We aim to improve the precision of AD diagnosis and facilitate its early identification. Initially, we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation, which are commonly observed in imaging datasets. Subsequently, an attention mechanism is incorporated to selectively focus on the salient… More >

Zhihua Liu, Shengquan Liu^*, Jian Zhang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 411-433, 2024, DOI:10.32604/cmc.2023.046237

Abstract Network intrusion detection systems (NIDS) based on deep learning have continued to make significant advances. However, the following challenges remain: on the one hand, simply applying only Temporal Convolutional Networks (TCNs) can lead to models that ignore the impact of network traffic features at different scales on the detection performance. On the other hand, some intrusion detection methods consider multi-scale information of traffic data, but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features. To address both of these issues, we propose a hybrid Convolutional Neural Network that supports a multi-output strategy (BONUS) for… More >

Yaoyao Du, Xiangkui Jiang^*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 303-327, 2024, DOI:10.32604/cmc.2023.046068

Abstract To address the challenges of high complexity, poor real-time performance, and low detection rates for small target vehicles in existing vehicle object detection algorithms, this paper proposes a real-time lightweight architecture based on You Only Look Once (YOLO) v5m. Firstly, a lightweight upsampling operator called Content-Aware Reassembly of Features (CARAFE) is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles, reducing the missed detection rate and false detection rate. Secondly, a new prediction layer for tiny targets is added, and the feature fusion network is redesigned to enhance the… More >

Tianmin Deng^*, Xiyue Zhang, Xinxin Cheng

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 533-549, 2024, DOI:10.32604/cmc.2023.044639

Abstract Vehicle detection plays a crucial role in the field of autonomous driving technology. However, directly applying deep learning-based object detection algorithms to complex road scene images often leads to subpar performance and slow inference speeds in vehicle detection. Achieving a balance between accuracy and detection speed is crucial for real-time object detection in real-world road scenes. This paper proposes a high-precision and fast vehicle detector called the feature-guided bidirectional pyramid network (FBPN). Firstly, to tackle challenges like vehicle occlusion and significant background interference, the efficient feature filtering module (EFFM) is introduced into the deep network, which amplifies the disparities between… More >

Tohid Adibi¹, Shams Forruque Ahmed^2,*, Omid Adibi³, Hassan Athari⁴, Irfan Anjum Badruddin⁵, Syed Javed⁵

Intelligent Automation & Soft Computing, Vol.38, No.1, pp. 103-103, 2023, DOI:10.32604/iasc.2023.047521

Abstract This article has no abstract. More >