Improved Data Stream Clustering Method: Incorporating KD-Tree for Typicality and Eccentricity-Based Approach

Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.

Assessing the effect of traditional Chinese medicine on CD4+ lymphocyte count of 807 HIV/AIDS cases

National Free Traditional Chinese Medicine (TCM) HIV/AIDS Treatment Program had been carried out for more than 5 years, treating 9267 cases accumulately by 2009. We report the 3-year outcome on CD4+ lymphocyte count of 807 cases of HIV/AIDS enrolled in the National Free TCM HIV/AIDS Treatment Pro- gram, the CD4+ lymphocyte count were measured every 6 month at 7 time points (0, 6, 12, 18, 24, 30, 36 month). The results showed that the overall CD4+ ly mphocyte count maintained stable at the 6th month and the 12th month, declined significantly at the 18th month, 24th month and 30th month, then elevated to the pre-treatment level at the 36th month. Patients with pre-treatment CD4+ lymphocyte count level 350/mm3 had CD4+ lymphocyte count declined significantly after all visits. In summary, combined treatment of Chinese herbal medicine and conventional therapy on HIV/AIDS suggested promising effect, but more evidences from larger, rigorous designed studies still needed to support the affirmative effect of TCM in the future.

针灸配合拔罐及回旋灸治疗痤疮40例临床观察（英文）

目的：观察针灸配合拔罐及回旋灸治疗痤疮的临床疗效。方法：将70例患者,按4︰3的比例随机分为针罐组40例和西药组30例。针罐组采用针刺配合拔罐及回旋灸治疗,隔天治疗1次,连续治疗30天。西药组采用常规口服西药米诺环素胶囊,每天2次,连续30天。结果：治疗后针罐组及西药组的总有效率分别为95.00﹪和76.67﹪,差异有统计学意义（P〈0.05）。在针罐组中以肺胃蕴热型患者疗效最好,总有效率为100%,肠胃湿热型和痰湿瘀滞型总有效率分别为71.43%、70.00%。结论：针灸配合拔罐治疗痤疮的临床疗效优于常规西药米诺环素胶囊治疗,值得临床推广应用。

Traffic signal detection and classification in street views using an attention model

Detecting small objects is a challenging task.We focus on a special case:the detection and classification of traffic signals in street views.We present a novel framework that utilizes a visual attention model to make detection more efficient,without loss of accuracy,and which generalizes.The attention model is designed to generate a small set of candidate regions at a suitable scale so that small targets can be better located and classified.In order to evaluate our method in the context of traffic signal detection,we have built a traffic light benchmark with over 15,000 traffic light instances,based on Tencent street view panoramas.We have tested our method both on the dataset we have built and the Tsinghua–Tencent 100K(TT100K)traffic sign benchmark.Experiments show that our method has superior detection performance and is quicker than the general faster RCNN object detection framework on both datasets.It is competitive with state-of-theart specialist traffic sign detectors on TT100K,but is an order of magnitude faster.To show generality,we tested it on the LISA dataset without tuning,and obtained an average precision in excess of 90%.

Harmonic Damping Characteristics of Single-phase Full-bridge Rectifier Based Loads

With the rapid development of power electronic technology,many linear single-phase loads in low voltage systems are replaced by those equipped with a single-phase diode rectifier.Therefore,most of single-phase loads become harmonic producers due to their nonlinear characteristics.The nonlinear loads may have different damping characteristics at harmonic frequencies,which could cause potential power quality problems.This paper studies the harmonic damping characteristics of such nonlinear loads.The harmonic damping index is defined to quantify the damping effect of nonlinear loads of harmonic frequencies by analyzing the harmonic model.Then,the harmonic damping index of different harmonic frequencies is compared through simulations and experiments.The results show that the nonlinear load has a constant damping effect for the fundamental frequency,but a stronger damping for the harmonic frequencies.The harmonic damping of such nonlinear loads varies with the harmonic phase angle of supply voltage,and becomes stronger with the increase of the harmonic order.It implies that this type of nonlinear load may be beneficial to the suppression of harmonic resonance.